System and method for using a mobile device as an input device for surveys at a live event

ABSTRACT

A method is provided for interacting with audience members in an event, each of the potential attendees having available thereto a unique identifier. The method comprises creating, for an attendee, a unique ID (UID) on a mobile wireless device (MWD) by the steps of inputting to the MWD one of the unique identifiers, combining the obtained unique identifier with a UID time stamp at the time of creation of the UID; receiving with a server on a first wireless channel communications from the MWD; registering the UID at the physical location of the event; generating a visual query; displaying on the MWD response indicators; receiving at the server from the registered attendee a response, to the query over the first wireless channel; and storing in a database on the server the received response in association with the displayed query.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation-in-Part of U.S. application Ser. No.15/453,226 filed on Mar. 8, 2017, entitled SYSTEM AND METHOD FOR USING AMOBILE DEVICE AS AN INPUT DEVICE FOR SURVEYS AT A LIVE EVENT. U.S.application Ser. No. 15/453,226 is incorporated by reference herein inits entirety. U.S. application Ser. No. 15/453,226 is aContinuation-in-Part of U.S. application Ser. No. 15/402,738, filed onJan. 10, 2017, entitled SYSTEM AND METHOD FOR USING A MOBILE DEVICE ASAN INPUT DEVICE FOR SURVEYS AT A LIVE EVENT. U.S. application Ser. No.15/402,738 is incorporated by reference herein in its entirety. U.S.application Ser. No. 15/402,738 is a Continuation-in-Part of U.S. patentapplication Ser. No. 15/360,697, filed on Nov. 23, 2016, entitled SYSTEMAND METHOD FOR USING A MOBILE DEVICE AS AN INPUT DEVICE FOR SURVEYS AT ALIVE EVENT, which is incorporated by reference herein in its entirety.U.S. patent application Ser. No. 15/360,697 is a Continuation-in-Part ofU.S. patent application Ser. No. 15/146,464, filed on May 4, 2016,entitled SYSTEM AND METHOD FOR CREATION OF UNIQUE IDENTIFICATION FOR USEIN GATHERING SURVEY DATA FROM A MOBILE DEVICE AT A LIVE EVENT, whichclaims the benefit of U.S. Provisional Application No. 62/258,988, filedon Nov. 23, 2015, entitled SYSTEM AND METHOD FOR EXTRAPOLATINGSTATISTICAL DATA GENERATED FROM A MOBILE DEVICE AT A LIVE EVENT, whichis incorporated by reference herein in its entirety. U.S. patentapplication Ser. No. 15/146,464 also claims the benefit of U.S.Provisional Application No. 62/258,982, filed on Nov. 23, 2015, entitledSYSTEM AND METHOD FOR CREATION OF UNIQUE IDENTIFICATION FOR USE INGATHERING SURVEY DATA FROM A MOBILE DEVICE AT A LIVE EVENT, which isincorporated by reference herein in its entirety. U.S. patentapplication Ser. No. 15/146,464 also claims the benefit of U.S.Provisional Application No. 62/258,983, filed on Nov. 23, 2015, entitledMETHOD FOR TRACKING ATTENDEE PARTICIPATION IN USING A SOFTWAREAPPLICATION AT A LIVE EVENT, which is incorporated by reference hereinin its entirety. U.S. patent application Ser. No. 15/146,464 also claimsthe benefit of U.S. Provisional Application No. 62/258,985, filed onNov. 23, 2015, entitled SYSTEM AND METHOD FOR USING A MOBILE DEVICE ASAN INPUT DEVICE FOR SURVEYS AT A LIVE EVENT, which is incorporated byreference herein in its entirety. U.S. patent application Ser. No.15/146,464 also claims the benefit of U.S. Provisional Application No.62/258,987, filed on Nov. 23, 2015, entitled SYSTEM AND METHOD FORFACILITATING A PURCHASE USING CARRIER INFORMATION FOR A MOBILE DEVICE,which is incorporated by reference herein in its entirety. U.S. patentapplication Ser. No. 15/146,464 is incorporated by reference herein inits entirety.

U.S. patent application Ser. No. 15/360,697 also claims the benefit ofU.S. Provisional Application No. 62/258,994, filed on Nov. 23, 2015,entitled SYSTEM AND METHOD FOR PROVIDING MOBILE DEVICE SURVEY INTERFACEBASED ON VISUAL INDICIA, U.S. Provisional Application No. 62/258,996,filed on Nov. 23, 2015, entitled SYSTEM AND METHOD FOR GENERATING ADIGITAL IMAGE BASED ON INPUT FROM MOBILE DEVICE TO ALLOW CROWD TOCONTROL VISUAL RESULTS OF RESPONSE, U.S. Provisional Application No.62/258,989, filed on Nov. 23, 2015, entitled SYSTEM AND METHOD FORSOCIAL MEASUREMENT OF INDIVIDUALS BASED ON DATA COLLECTED FROM MOBILEDEVICE, U.S. Provisional Application No. 62/258,997, filed on Nov. 23,2015, entitled SYSTEM AND METHOD FOR CONDUCTING A CONTEST BASED ON INPUTFROM MOBILE DEVICE IN A CROWD-BASED RESPONSE SYSTEM, and U.S.Provisional Application No. 62/258,982, filed on Nov. 23, 2015, entitledSYSTEM AND METHOD FOR CREATION OF UNIQUE IDENTIFICATION FOR USE INGATHERING SURVEY DATA FROM A MOBILE DEVICE AT A LIVE EVENT. U.S. patentapplication Ser. No. 15/360,697 also claims the benefit of U.S.Provisional Application No. 62/258,983, filed on Nov. 23, 2015, entitledMETHOD FOR TRACKING ATTENDEE PARTICIPATION IN USING A SOFTWAREAPPLICATION AT A LIVE EVENT, U.S. Provisional Application No.62/258,985, filed on Nov. 23, 2015, entitled SYSTEM AND METHOD FOR USINGA MOBILE DEVICE AS AN INPUT DEVICE FOR SURVEYS AT A LIVE EVENT, U.S.Provisional Application No. 62/258,987, filed on Nov. 23, 2015, entitledSYSTEM AND METHOD FOR FACILITATING A PURCHASE USING CARRIER INFORMATIONFOR A MOBILE DEVICE, U.S. Provisional Application No. 62/258,988, filedon Nov. 23, 2015, entitled SYSTEM AND METHOD FOR EXTRAPOLATINGSTATISTICAL DATA GENERATED FROM A MOBILE DEVICE AT A LIVE EVENT, andU.S. Provisional Application No. 62/258,990, filed on Nov. 23, 2015,entitled SYSTEM AND METHOD FOR EXTRAPOLATING STATISTICAL DATA GENERATEDFROM A MOBILE DEVICE AT A LIVE EVENT FOR DETERMINING MERCHANTABILITY.U.S. Provisional Application No. 62/258,994 is incorporated by referenceherein in its entirety. U.S. Provisional Application No. 62/258,996 isincorporated by reference herein in its entirety. U.S. ProvisionalApplication No. 62/258,989 is incorporated by reference herein in itsentirety. U.S. Provisional Application No. 62/258,997 is incorporated byreference herein in its entirety. U.S. Provisional Application No.62/258,982 is incorporated by reference herein in its entirety. U.S.Provisional Application No. 62/258,983 is incorporated by referenceherein in its entirety. U.S. Provisional Application No. 62/258,985 isincorporated by reference herein in its entirety. U.S. ProvisionalApplication No. 62/258,987 is incorporated by reference herein in itsentirety. U.S. Provisional Application No. 62/258,988 is incorporated byreference herein in its entirety. U.S. Provisional Application No.62/258,990 is incorporated by reference herein in its entirety.

U.S. application Ser. No. 15/402,738 also claims the benefit of U.S.Provisional Application No. 62/277,270, filed on Jan. 11, 2016, entitledSYSTEM AND METHOD FOR USING DATA FROM A MOBILE DEVICE SURVEY SYSTEM FORMESSAGE SELECTION AT A LIVE EVENT. U.S. application Ser. No. 15/402,738also claims the benefit of U.S. Provisional Application No. 62/277,888,filed on Jan. 12, 2016, entitled SYSTEM AND METHOD FOR FACILITATING APURCHASE FROM A VENDOR USING CARRIER INFORMATION FOR A MOBILE DEVICE.U.S. application Ser. No. 15/402,738 also claims the benefit of U.S.Provisional Application No. 62/277,941, filed on Jan. 12, 2016, entitledSYSTEM AND METHOD FOR USING DATA FROM MOBILE DEVICES WITHIN A WIRELESSMESH NETWORK. U.S. application Ser. No. 15/402,738 also claims thebenefit of U.S. Provisional Application No. 62/277,899, filed on Jan.12, 2016, entitled SYSTEM AND METHOD FOR BUILDING A SOCIAL MEASUREMENTMATRIX. U.S. application Ser. No. 15/402,738 also claims the benefit ofU.S. Provisional Application No. 62/277,903, filed on Jan. 12, 2016,entitled SYSTEM AND METHOD FOR SEASONAL EVENT DEPENDENT REPORTING. U.S.application Ser. No. 15/402,738 also claims the benefit of U.S.Provisional Application No. 62/277,914, filed on Jan. 12, 2016, entitledSYSTEM AND METHOD FOR EVENT BASED INTERACTIVE POLLING. U.S. applicationSer. No. 15/402,738 also claims the benefit of U.S. ProvisionalApplication No. 62/277,917, filed on Jan. 12, 2016, entitled SYSTEM ANDMETHOD FOR DETERMINING COMPARATIVE LIFESTYLE TRIGGERS. U.S. applicationSer. No. 15/402,738 also claims the benefit of U.S. ProvisionalApplication No. 62/277,943, filed on Jan. 12, 2016, entitled SYSTEM ANDMETHOD FOR GENERATING POLLING RESULTS FROM INPUTS PROVIDED AT THE SUPERBOWL. U.S. application Ser. No. 15/402,738 also claims the benefit ofU.S. Provisional Application No. 62/277,918, filed on Jan. 12, 2016,entitled SYSTEM AND METHOD FOR USING DATA FROM MOBILE DEVICES WITHIN AGEOFENCE. U.S. Provisional Application No. 62/277,270 is incorporated byreference herein in its entirety. U.S. Provisional Application No.62/277,888 is incorporated by reference herein in its entirety. U.S.Provisional Application No. 62/277,941 is incorporated by referenceherein in its entirety. U.S. Provisional Application No. 62/277,899 isincorporated by reference herein in its entirety. U.S. ProvisionalApplication No. 62/277,903 is incorporated by reference herein in itsentirety. U.S. Provisional Application No. 62/277,914 is incorporated byreference herein in its entirety. U.S. Provisional Application No.62/277,917 is incorporated by reference herein in its entirety. U.S.Provisional Application No. 62/277,943 is incorporated by referenceherein in its entirety. U.S. Provisional Application No. 62/277,918 isincorporated by reference herein in its entirety.

U.S. application Ser. No. 15/453,226 also claims benefit of U.S.Provisional Application No. 62/305,345, filed on Mar. 8, 2016, entitledSYSTEM AND METHOD FOR PREDICTIVE CONSUMER TRENDS ENGINE. Thisapplication also claims benefit of U.S. Provisional Application No.62/305,354, filed on Mar. 8, 2016, entitled SYSTEM AND METHOD FORGENERATING REMOTE SOCIAL ANALYTICS BASED ON REGIONALLY LOCATEDINTERACTIVE SERVERS. This application also claims benefit of U.S.Provisional Application No. 62/305,349, filed on Mar. 8, 2016, entitledSYSTEM AND METHOD FOR GENERATING LIFESTYLE REPORTS BASED ON LIVE EVENTS.This application also claims benefit of U.S. Provisional Application No.62/305,408, filed on Mar. 8, 2016, entitled SYSTEM AND METHOD FOR REALTIME DIGITAL MEASUREMENT. This application also claims benefit of U.S.Provisional Application No. 62/305,417, filed on Mar. 8, 2016, entitledSYSTEM AND METHOD FOR GENERATING TIME SENSITIVE SOCIAL MEASUREMENT. Thisapplication also claims benefit of U.S. Provisional Application No.62/305,420, filed on Mar. 8, 2016, entitled SYSTEM AND METHOD FORDEVELOPING DIGITAL MEDIA PRODUCTION BASED ON POTENTIAL DECISION TREE.This application also claims benefit of U.S. Provisional Application No.62/305,427, filed on Mar. 8, 2016, entitled SYSTEM AND METHOD FORPROVIDING SOCIAL SENSITIVE MEDIA SERVING. This application also claimsbenefit of U.S. Provisional Application No. 62/305,431, filed on Mar. 8,2016, entitled SYSTEM AND METHOD FOR DIGITAL MEDIA CREATION BASED ONDETERMINING THE MOST DESIRED PRODUCT FEATURES. This application alsoclaims benefit of U.S. Provisional Application No. 62/316,745, filed onApr. 1, 2016, entitled SYSTEM AND METHOD FOR SPONTANEOUS EVENT POLLING.This application also claims benefit of U.S. Provisional Application No.62/319,161, filed on Apr. 6, 2016, entitled SYSTEM AND METHOD FORGENERATING POLLING RESULTS FROM INPUTS PROVIDED AT A NASCAR EVENT. U.S.Provisional Application Nos. 62/305,345, 62/305,354, 62/305,349,62/305,408, 62/305,417, 62/305,420, 62/305,427, 62/305,431, 62/316,745and 62/319,161 are incorporated by reference herein in their entirety.

This application also claims benefit of U.S. Provisional Application No.62/347,927, filed on Jun. 9, 2016, entitled SYSTEM AND METHOD FORPRE-EVENT FAN SEGMENTING. This application also claims benefit of U.S.Provisional Application No. 62/347,940, filed on Jun. 9, 2016, entitledSYSTEM AND METHOD FOR ANALYZING TRANSIENT MOMENTS IN A LIVE EVENT. Thisapplication also claims benefit of U.S. Provisional Application No.62/347,946, filed on Jun. 9, 2016, entitled SYSTEM AND METHOD FORINTER-APPLICATION SEGMENTING. This application also claims benefit ofU.S. Provisional Application No. 62/350,966, filed on Jun. 16, 2016,entitled SYSTEM AND METHOD FOR SOCIAL VOTING CONTENT. This applicationalso claims benefit of U.S. Provisional Application No. 62/350,984,filed on Jun. 16, 2016, entitled SYSTEM AND METHOD FOR DIGITAL VIDEOBUILD BASED ON VENUE. This application also claims benefit of U.S.Provisional Application No. 62/347,954, filed on Jun. 9, 2016, entitledSYSTEM AND METHOD FOR S/R/S/VENUE TRANSLATION TO UNIQUE IDENTIFICATIONFOR CLASSIFICATION AND PROFILE. This application also claims benefit ofU.S. Provisional Application No. 62/347,957, filed on Jun. 9, 2016,entitled SYSTEM AND METHOD FOR AUGMENTED SOCIAL IDENTIFICATION. Thisapplication also claims benefit of U.S. Provisional Application No.62/350,999, filed on Jun. 16, 2016, entitled SYSTEM AND METHOD FORGENERATING REMOTE SOCIAL ANALYTICS BASED ON REGIONALLY LOCATEDINTERACTIVE SERVERS. This application also claims benefit of U.S.Provisional Application No. 62/351,015, filed on Jun. 16, 2016, entitledSYSTEM AND METHOD FOR MODIFYING ADVERTISEMENT DELIVERY BASED ON VENUESOCIAL MEDIA CHECK-IN. This application also claims benefit of U.S.Provisional Application No. 62/350,819, filed on Jun. 16, 2016, entitledSYSTEM AND METHOD FOR EXTRAPOLATING STATISTICAL DATA GENERATED FROM AMOBILE PHONE AT A LIVE EVENT. This application also claims benefit ofU.S. Provisional Application No. 62/350,826, filed on Jun. 16, 2016,entitled SYSTEM AND METHOD FOR UNIQUE IDENTIFICATION CREATION BASED ONUNIQUE TICKET INFORMATION WITHIN A VENUE. This application also claimsbenefit of U.S. Provisional Application No. 62/350,838, filed on Jun.16, 2016, entitled SYSTEM AND METHOD FOR USING UNIQUE TICKET INFORMATIONTO LOCATE AN ATTENDEE IN A LIVE EVENT VENUE. This application alsoclaims benefit of U.S. Provisional Application No. 62/350,911, filed onJun. 16, 2016, entitled SYSTEM AND METHOD FOR USING UNIQUE TICKETINFORMATION TO DELIVER ITEMS TO AN ATTENDEE IN A LIVE EVENT VENUE. Thisapplication also claims benefit of U.S. Provisional Application No.62/350,947, filed on Jun. 16, 2016, entitled SYSTEM AND METHOD FOR USINGIDENTIFIABLE VISUAL CUES IN AN EVENT VENUE TO PROMPT INTERACTION WITHMOBILE DEVICE. This application also claims benefit of U.S. ProvisionalApplication No. 62/351,026, filed on Jun. 16, 2016, entitled SYSTEM ANDMETHOD FOR GENERATING REMOTE SOCIAL ANALYTICS BASED ON REGIONALLYLOCATED INTERACTIVE SERVERS. U.S. Provisional Application Nos.62/347,927, 62/347,940, 62/347,946, 62/350,966, 62/350,984, 62/347,954,62/347,957, 62/350,999, 62/351,015, 62/350,819, 62/350,826, 62/350,838,62/350,911, 62/350,947, and 62/351,026 are incorporated by referenceherein in their entirety.

TECHNICAL FIELD

The following disclosure relates to generally to the interface betweenadvertisers, media companies, leagues, sponsors, underwriters, partnersand media partners, leagues, teams, franchises, sponsors, underwriters,media partners, conferences, venue specific messengers, andchampionships, as well as a target audience, and collecting statisticsrelating thereto.

BACKGROUND

When advertisers, media companies, leagues, sponsors, underwriters,partners and media partners, leagues, teams, franchises, sponsors,underwriters, media partners, conferences, venue specific messengers,and championships (“the messenger”) distribute their advertising withrespect to a particular venue, it is important that they have some typeof feedback as to the effectiveness of these advertisements. The mainproblem that exists today in certain venues is that the advertisement isdisplayed on a screen at, for example, a football game, and it isexpected that a certain portion of the attendees are viewing the screen.However, some attendees may have left their seats and gone forrefreshments or they may actually, in the current environment, theoccupied with their mobile devices. As such, it is difficult for anadvertiser to have any feedback as to the “effectiveness” of aparticular advertisement at reaching the eyes of the attendees.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding, reference is now made to thefollowing description taken in conjunction with the accompanyingDrawings in which:

FIG. 1 illustrates an overall diagrammatic view of a venue utilizing adisclosed embodiment;

FIG. 2 illustrates a diagrammatic view of multiple attendees interfacedwith a screen on which advertisements are presented;

FIG. 3 illustrates a view of a single attendee interfacing with thescreen and choices provided thereon and their mobile units and theselections provided thereon;

FIG. 4 illustrates a flowchart depicting the top level login operation;

FIG. 5 illustrates a flow chart illustrating the top level queryoperation;

FIGS. 6A-6C illustrate examples of the initial registration whenentering the venue;

FIG. 7 illustrates a flowchart for the overall registration operation;

FIG. 7A illustrates a flowchart depicting the payment operation;

FIG. 8 illustrates a flowchart depicting the overall operation ofcreating the unique ID;

FIG. 9 illustrates a flowchart depicting the operation of creating theunique ID at the user's device;

FIGS. 10A and 10B illustrate flowcharts for launching the applicationbased on a presence determination at the gate of the entrance;

FIG. 11 illustrates a diagrammatic view of the screen interface with theuser for entering the ticket information and creating the unique ID;

FIG. 11B illustrates a screen view illustrating the fixed selection ofpossible response buttons provided to a user;

FIG. 12 illustrates a flowchart depicting the query operation forgenerating a query for viewing by the user;

FIG. 13 illustrates the data structure of information assembled at andtransmitted by the Mobile Unit;

FIG. 14 illustrates a flowchart for a server receiving a response;

FIGS. 15-18 illustrate diagrammatic views of the various records thatare generated and populated in the local database;

FIG. 19 illustrates a diagrammatic view of the overall relationshipbetween multiple records and unique IDs in the system;

FIG. 20 illustrates a detail of the records illustrated in FIG. 19;

FIG. 21 illustrates a diagrammatic view of the template utilized forcreating a query;

FIG. 22 illustrates a flowchart for a process of tracking attendeeparticipation;

FIG. 23 illustrate a diagrammatic view of an alternate embodiment foroffering merchandise for sale;

FIG. 24 illustrates a diagrammatic view of the overall system forfacilitating the embodiment of FIG. 23;

FIG. 25 illustrates a flowchart for the overall operation of the systemof FIG. 23;

FIG. 26 illustrates a flowchart for ensuring that only a single seat isassociated with a unique ID;

FIG. 27 illustrates a flowchart for analyzing and collecting statisticsvia the crowd-based response input;

FIG. 28 illustrates a diagrammatic view of the relationship between thequery and the UID;

FIG. 29 illustrates the overall response that can be collected of thatparticular query;

FIG. 30 illustrates a diagrammatic view of the mapping of queries toSCIDs;

FIG. 31 illustrates a detailed example of a mapping of various QIDs toSCIDs;

FIG. 32 illustrates a diagrammatic view of one particular statisticalspread over multiple queries presented throughout an event at a liveperformance within a given venue;

FIG. 33 illustrates a flowchart depicting one embodiment of a processfor extrapolating from statistical data gathered from mobile devices ata live event;

FIG. 34 illustrates an overall flowchart of the audience participationfunction;

FIG. 35 illustrates a flowchart for the wave example of audienceparticipation;

FIG. 36 illustrates a diagrammatic view of the display for the waveresponse;

FIG. 37 illustrates a flowchart for the noise example of audienceparticipation;

FIG. 38 illustrates a diagrammatic view of the display for the noiseaudience participation response;

FIG. 39 illustrates a flowchart for the operation of running a contest;

FIG. 40 illustrates a diagrammatic view of populating the UID record fora particular mobile unit;

FIG. 41 illustrates sets of media messages which can be presented;

FIG. 42 illustrates sets of queries that can be presented;

FIG. 43 illustrates a flowchart showing how queries and media messagesare chosen and presented;

FIG. 44 illustrates the first part of an embodiment which uses a mediamessage as a commercial for a truck;

FIG. 45 illustrates the second part of an embodiment which uses a mediamessage in the form of a commercial for a truck;

FIG. 46 illustrates a stadium with attendees who respond to queries withtaps to their mobile devices;

FIG. 47 illustrates the embodiment of users checking-in on the mobiledevices;

FIG. 48 illustrates a branching decision tree of queries and mediamessages;

FIG. 49 illustrates the selection of a full-length music video based onselection from short video clips;

FIG. 50 illustrates how the analysis of queries responses are used toselect the next query;

FIG. 51 illustrates a flowchart showing the process by which a query isselected;

FIG. 52 illustrates a flowchart depicting another embodiment for anoperation of a merchandising feature area;

FIG. 53 illustrates a diagrammatic view of one embodiment of a systemfor providing items to be sold based on visual indicia within the rangeof a beacon;

FIG. 54 illustrates a flowchart depicting one embodiment of a method forpurchasing items to be sold based on visual indicia within the range ofa beacon;

FIG. 55 illustrates a stadium with several mesh networks;

FIG. 56 illustrates several overlapping mesh networks;

FIG. 57 illustrates several mesh networks connected to external networksvia a hotspot;

FIG. 58 illustrates an embodiment using a mesh network;

FIG. 59 illustrates another embodiment using a mesh network;

FIG. 60 illustrates yet another embodiment using a mesh network;

FIG. 61 illustrates a diagrammatic view of one embodiment a relationaldatabase trending determination model;

FIG. 62 illustrates one embodiment of an example database reportretrieved from a database in which query and response data is stored;

FIG. 63 illustrates a flowchart depicting one embodiment of a reportgeneration method;

FIG. 64 illustrates one embodiment of an example seasonal databasereport retrieved from a database in which query and response data isstored;

FIG. 65 illustrates a flowchart depicting one embodiment of a seasonalreport generation method;

FIG. 66 illustrates representations of multiple presentations and theresponses received from post-presentation queries;

FIG. 67 illustrates a flowchart showing the process of determininglifestyle triggers for presentations;

FIG. 68 illustrates a diagrammatic view of one embodiment of recognizingan individual entering a geo-fenced venue;

FIG. 69 illustrates a flowchart depicting one embodiment of ageo-fencing application trigger process;

FIG. 70 illustrates a diagrammatic view of one embodiment of aPredictive Consumer Trends Engine neural network;

FIG. 71 illustrates a diagrammatic view of another embodiment of aPredictive Consumer Trends Engine neural network;

FIG. 72 illustrates a diagrammatic view of one embodiment of aPredictive Consumer Trends Engine;

FIG. 73 illustrates a flowchart depicting one embodiment of a predictiveconsumer trends engine method;

FIG. 74 illustrates a diagrammatic view of one embodiment of a trainingdatabase;

FIG. 75 illustrates a diagrammatic view of a predictive engine beingtrained with information in a training database;

FIG. 76 illustrates a diagrammatic view of a trained predictive engine;

FIG. 77 illustrates a flowchart depicting one embodiment of a socialanalytics generation utilizing local servers method;

FIGS. 78A-78D illustrate graphs depicting the number of responses as afunction of time;

FIGS. 79A-79D illustrate graphs depicting response rates as a functionof time;

FIGS. 80A-80E illustrate graphs of response rates grouped by mobile unituser location;

FIGS. 81A-81D illustrate how response rates can be grouped bydemographic;

FIG. 82 illustrates a screen for selecting how to group statistics bydemographic;

FIG. 83 illustrates a screen for viewing real-time measurements;

FIG. 84 illustrates a flowchart for an embodiment which uses real-timemeasurements;

FIG. 85 illustrates a flowchart for an embodiment which uses a filterprocess;

FIG. 86 illustrates a flowchart for an embodiment of the displayinteraction process step depicted in the flowchart of FIG. 84;

FIG. 87 illustrates a flowchart for an embodiment of the subsequent pathcontrol function step depicted in FIG. 84;

FIG. 88 illustrates an embodiment in which a system operator interactswith the displayed video message in order to vary the presented videomessage;

FIG. 89 illustrates a flowchart for an embodiment in which responses areaccumulated and displayed as part of a sample window within a queryresponse window;

FIG. 90 illustrates a relational database correlating queries with queryresponse window times;

FIG. 91 illustrates a graph showing an example of how responses arecorrelated with queries;

FIG. 92 illustrates a relational database correlating query responseswith response meanings;

FIG. 93 illustrates a set of media messages for a potential decisiontree;

FIG. 94 illustrates multiple sets of media messages for a potentialdecision tree;

FIGS. 95A and 95B illustrate sets of media messages targeting particulargroups of viewers;

FIGS. 96A-96C illustrate the process of selecting media messages basedon data gathered from responses to queries;

FIG. 97 illustrates a plurality of presentations arranged in ahierarchy;

FIG. 98 illustrates an example of determining a desired product featureby using a hierarchy of presentations;

FIG. 99 illustrates a flowchart showing the process of determiningdesired product features and creating media having the same;

FIG. 100 illustrates another view of the embodiment depicted in FIG. 98;

FIG. 101 and illustrates an embodiment using weights to determine themost desired product feature;

FIG. 102 illustrates an embodiment using weights to determine the mostdesired product feature;

FIG. 103 illustrates a flow diagram of one embodiment of a spontaneousevent polling process;

FIG. 104 illustrates a flow diagram of another embodiment of aspontaneous event polling process;

FIG. 105 illustrates an event venue and a pre-event area;

FIG. 106 illustrates a graph depicting the event and pre-event UIDpopulations;

FIG. 107 illustrates a graph depicting the number of responses fromevent and pre-event UIDs submitted during a time window;

FIGS. 108A-C depict the responses to a query by different UIDpopulations;

FIG. 109 illustrates a flowchart depicting the process for creating apre-event UID;

FIG. 110 illustrates the relationships between a UID and UID populationdatabases;

FIG. 111 illustrates a chart depicting the number of responses fromevent, pre-event, and pre-event-only UIDs submitted during a timewindow;

FIGS. 112A-C depict the responses to a query by different UIDpopulations;

FIG. 113 illustrates several different areas of an event venue, eachwith its own query display;

FIG. 114 illustrates a timeline depicting responses submitted by a UID;

FIG. 115 illustrates a table for determining which area a query responseinput is associated with;

FIG. 116A illustrates a table for determining where a UID was locatedwhen it submitted a response;

FIG. 116B illustrates a timeline for where a UID was located when itsubmitted a query response;

FIGS. 117A-D illustrate charts showing the number of UIDs submittingresponses to queries in different areas of a venue;

FIG. 118 illustrates the use of a third-party application with built-insystem functionality;

FIG. 119 illustrates a flowchart for creating a UID with a third-partyapplication;

FIG. 120 illustrates a query displayed in a venue and an social mediapost based on the query and the user's response;

FIG. 121A illustrates the various networked devices used to generate andpost social media posts to users' social media profiles;

FIG. 121B illustrates another embodiment of the system for generatingsocial media posts based on queries and responses;

FIG. 122 is a flowchart depicting the process for associating queryresponse information with a UID;

FIGS. 123A-C are flowcharts depicting the details of various steps ofthe flowchart in FIG. 122;

FIG. 124 is a flowchart depicting the process by which a social mediapost based on queries and responses is created along with an appendedadvertisement or message;

FIG. 125 illustrates examples of different digital video builds;

FIG. 126 is a flowchart depicting the process for appending a digitalvideo build to a social media post;

FIG. 127A illustrates a UID structured as a data structure;

FIG. 127B illustrates a UID appended with a venue-specific data field;

FIGS. 127C-E illustrate charts depicting how attendees in differentvenues responded to a query;

FIGS. 127F-H illustrate charts depicting the gender make-up of attendeesat different venues;

FIG. 127I illustrates a flowchart depicting the process forautomatically appending a venue-specific data field to a UID datastructure;

FIG. 128 illustrates a UID structured as a data structure that containsa data field having information associated with a pre-existing socialnetwork I.D.;

FIG. 129 depicts the registration screen of the application on a mobileunit which allows for the selection of a social network associated withthe social network I.D.;

FIG. 130 illustrates a UID structured as a data structure that containsa data field having information associated with a pre-existing socialnetwork I.D and a data field indicating to which social media networkthe social network I.D. is associated;

FIG. 131 illustrates an embodiment in which digital videos are stored ona media server;

FIG. 132 illustrates an embodiment where several digital videos arestored on a media server and served to multiple social media users;

FIG. 133 illustrates a flowchart showing the process for delivering adigital video from a media server;

FIG. 134 illustrates a social media application with enhanced built-infunctionality;

FIG. 135 is a flowchart depicting the process of generating a UID via asocial media application;

FIG. 136 illustrates an embodiment wherein a user checks-in at a liveevent with a social media application;

FIG. 137 illustrates a relational table used to determine which event auser is at;

FIGS. 138A-B illustrate charts showing differences in users whochecked-in at an event and general population users;

FIG. 139 illustrates a view of a social media application featuringevent-specific advertisements;

FIG. 140 is a timeline depicting optimal times for the delivery ofadvertisements;

FIG. 141 is a flowchart depicting the process of distributingadvertisements based on an event-based schedule; and

FIG. 142 illustrates the communication path by which social media andvenue information is communicated from the mobile unit to the socialmedia servers.

DETAILED DESCRIPTION

Referring now to FIG. 1, there is illustrated a diagrammatic view of theoverall operation at a particular venue utilizing the disclosedembodiment. In this illustration, there is illustrated a single venue102, such as a football stadium, a concert hall, or anything thatrequires a ticket to grant entrance thereto and also provide some typeof seating chart such that each ticket holder has a defined seatassociated therewith or assigned thereto. This venue 102 has providedtherefore, by example, two gates 104 and 106. In the center of the venue102 or disposed throughout the venue 102 there is provided some type ofvisual/audio interfaced 108. Throughout the following description, thiswill typically be referred to as a visual interface providing a visualcue of some sort. However, it should be understood that the cue is sometype of information that can be transmitted from one or more locationswithin the venue 102 in the form of a video or an audio cue or some typeof cue that can be sensed by an attendee. Although this visual cue 108is illustrated as being in the center of the venue 102, it should beunderstood that it can be located at different locations throughout thevenue 102. Additionally, the visual cue from multiple locations couldall be the same cue, or it could actually be different cues.

There are illustrated a plurality of Mobile Units 110 labeled “M” whichwill be referred to hereinafter by the terminology “MU” 110. Each ofthese MUs 110 is associated with an individual, and that individual hasassociated therewith a ticket, this ticket referred to by a referencenumeral 112. The only MUs that are illustrated as having a ticket 112associated therewith are those that are entering the gate 104 or thegate 106. Each of these MUs 110 has the ability to communicate via awireless link to one of the plurality of wireless network receivers 116disposed throughout the venue 102. These wireless network receiversprovide substantially full coverage around the venue 102, and each ofthe wires receivers 116 are connected directly to a local central office120 (CO) which basically has a computer that is interfaced with a localdatabase 122. This database 122 and local central office 120 areconnected through a global network 124 (Internet) to a central remoteoffice 126, which has associated therewith a central database 126.

The wireless receivers can be any type of wireless receiver network, forexample, a Wi-Fi-based network. However, it should be understood thatany other type of network could be utilized. Each of these wirelessreceivers 116 has associated therewith a unique ID in the form of anSSID that can be recognized by the MU 110 and, once a communication linkis effected between the MU 110 and the wireless receiver 116, a physicallocation can be established with respect to the physical location of thevenue 102. Since the local central office 120 is aware of its locationand it is connected directly to the wireless receivers 116, the locationof the venue 102 can be associated with any data in the local database122. This allows any data associated with the local database 122 to alsobe associated with any information collected from attendees at the eventoccurring in the venue 102.

Additionally, the wireless interface between each of the MUs 110 and thelocal central office 120 could be effected with a mesh network. Thecommunication protocol could use a Zigbee network, a Thread network, orany type of network that allows data to actually be transmitted to amaster station to be transferred from one MU 110 to another MU 110.

In the overall operation, as will be described hereinbelow, a particularuser will enter the venue 102 and initiate an application on theirassociated MU 110 which will create a unique ID (UID) associated withthat particular device at that particular time based upon informationcontained on their individual ticket which will also identify the seatto which they are assigned. The user will then provide a response ofsome sort to possibly a visual cue received locally and send the UID andresponse to the local CO 120. This will result in a registration of thatparticular device with the local CO 120. Thereafter, visual cues aredisplayed on the display 108 with choices. These choices are associatedwith preset choice buttons on the MU 110 that, when selected, provideresponses that are utilized by the local CO 120 for collectingstatistics on the attendees.

Referring now to FIG. 2, there is illustrated a diagrammatic view of aplurality of individuals 202 with their associated MUs 110. The display108 is illustrated as providing a visual cue in the form of some type ofprogram, advertisement or the such that will be followed with orassociated with a visual cue that, if the individual 102 is viewing thescreen and is paying attention to the advertisement, will be enticed toactually make a selection and, upon making a selection, this selectionor responses sent back via the wireless receiver 116 two the local CO120.

Referring now to FIG. 3, there is illustrated a diagrammatic view of asingle individual presented with three responses on the display 108,these being illustrated as the letter A, the letter B, and the letter C.Each of these is associated with some type of information which allowsthe individual 202 to discern these particular choices. They may be sometype of contest providing different selections. It may be that theparticular cue requires a single response just to indicate that the useris paying attention to the screen. For example, it could be a contestthat allows a responder the possibility of entering a contest, i.e.,“press A on your device to enter your seat number in a lottery to win acertain prize.” The MU 110 is provided thereon a screen 302 having thosethree selected letters available for choices. By placing their fingerover one of the selections, the user creates a response that is thencombined with a timestamp 304 and the created UID 306 back to the localCO 120 for processing thereof. It should be understood that, once theUID is created by the MU 110, this is now a UID that is carriedtemporarily in the MU 110 until the MU 110 either leaves the venue 102or there is some type of timeout period of, for example, two hours.

The result of this overall operation is that a device, once entering thegate and initiating the application, creates a UID on the device thatdefines that device in a local database. Thereafter, any response can becorrelated with the query in the substance of that query as long as theresponse is sent within a particular time window. For example, a querywould be transmitted to the attendees and, during the transmission orslightly thereafter, there is a defined time window within which aresponse must be made. As such, even though the button associated withthe letter A is selected for different queries, it is easy todiscriminate in the database what information that particular responsewas associated with.

Referring now to FIG. 4, there is illustrated a flowchart of the toplevel login operation, which is initiated at a block 402. This thenproceeds to a decision block 404 to determine if a new user has enteredthe system. This is typically determined at the gate when the userpasses through the gate or when an application is initiated. This mayalso be determined when a user answers an initial query, in addition toproviding the user's seat number. If it is indicated that a new user ispresent, the program proceeds along a “Y” path to a block 406 to loginan initial unique ID (UID) for that device. The program then proceeds toa function block 408 in order to register payment at the login event foreach instance of a device passing through the gate or initiating theirapplication. This payment operation will be described in more detailbut, in general, the way that revenue is collected on this particularoverall operation is that a flat fee is provided for each device that isregistered for a particular event. The flat fee may be for any value.Thereafter, all of the data collected, whether the data is voluminous ornot is immaterial to the overall revenue-generating model. Thus, then adefined amount of money can be collected depending upon the number ofattendees while the advertisement level or volume has no effect on theoverall revenue model. However, data is, to a large extent, owned by thecentral office. After registration of the login instance and theregistration of the payment for that instance, a new object is createdfor that new UID in the local database, as indicated by a block 410. Theflowchart then loops back to the beginning.

Referring now to FIG. 5, there is illustrated a flowchart for the toplevel query operation, which is initiated at a block 502. The programthen flows to a block 504 in order to select a new query from the queueof queries. In general, when the system is set up, there will typicallybe some type of programming control over the information that ispresented to the attendees at the event in the venue 102. This will,from overall point of view, allow each query to be independent withinthe database. However, they will be placed in the queue so that they canbe individually selected at particular times and associated withparticular advertisements. The program then flows to a block 506 todetermine if the query has been initiated, which will occur at a definedtime within the overall program schedule. The program then flows to ablock 508 in order to set a window counter to a null value. As describedhereinabove, each query requires a response to be returned within adefined time window. This actually gives context and meaning to aresponse. Otherwise, a simple key interface with a defined set ofsymbols, letters, or numbers would not be possible. In this matter, theletter A can be used multiple times for multiple queries and have adifferent meaning associated there with any statistical analysis of theoverall data structure.

The program then flows to a function block 510 after it has beeninitiated and sent to a null value to run the visual cue. This way theysee some type of advertisement with some type of enticing responserequired. The program will then flows to a function block 512 in orderto process all of the responses received within the window, each of theresponses having a timestamp associated therewith such that onlyresponses received with a timestamp within the query window will belogged. The program then flows to a decision block 514 to determine ifthe counter is a maximum value, i.e., the end of the query time window.If not, the program flows along the “N” path to a block 516 in order toincrement the counter and then back to the input of the block 510. Thiswill occur until the counter has reached its maximum value, at whichtime flowchart will back around to the input of the block 504 to selectthe next query.

Referring now to FIGS. 6A-6C, there are illustrated three differentdiagrammatic views of how the presence of an individual 202 isrecognized at one of the gates 104 or 106. In the embodiment of FIG. 6A,the individual 202 has the ticket 112 associated therewith, and, uponreaching gate, the individual 202 is prompted by some type of signage orthe such to activate their application. Upon activating theirapplication, the individual 202 can be presented with a screen to selecta particular response which, when transmitted to the wireless device 116with the created UID of the MU 110 and information regarding theselected response. As will be described hereinbelow, that is defined asa Response ID (RID). The second embodiment associated with FIG. 6B, theindividual 202 is recognized by a beacon 602 which generates a signalthat can be scanned by a separate receiver on the MU 110. Thesetypically operate under IEEE 802.15.XX protocol, and they typically havesome type of unique ID associated therewith and, in some instances,especially with the beacon, a command structure that allows more thanjust an ID to be sent. These can be a Bluetooth system or a BLE systemor a Zigbee system or other similar systems. The point is that theapplication running on the MU 110 can recognize this ID and, uponrecognizing this ID, can launch the full program and display the screento the individual 202. The individual 202 then enters the ticket numberand the MU 110 then creates the UID and generates a response, i.e., itanswers a question which is an initial question, and then transmits thisto the wireless device 116 for transmission to the local CO 120 forregistration.

In the embodiment of FIG. 6C, there is illustrated an embodiment whereinthe individual answers the initial question via some type of visual cuethat is presented at the gate. This is a special visual cue that may bepermanent. The user must answer this question in order to be registered.The screen of the user may actually display a simple display indicatingto the user that they must view this visual cue at the gate and enter itin order to be eligible for a prize. This will prompt the individual 202to input information from the ticket in additional to answering theresponse. Again, what is required to register the particular device withthe local CO 120 is to generate UID from the ticket and then answer aquestion and provide one of one or more available responses to thatquestion and forwarded the UID and RID to the local CO 120.

Referring now to FIG. 7, there is illustrated a flowchart depicting theoverall detection of the presence of an individual at a gate, which isinitiated at a block 702 and then proceeds to a decision block 704. Thisdecision block 704 determines if it has detected the presence of a newdevice entering the venue 102 and, if so, the program proceeds to afunction block 706 to register that user in the database as an instance,i.e., it creates a new record for that individual device which,thereafter, when it receives the UID from that device in associated witha response, it can recognize that a particular device has responded.This is important in that, for example, an individual might respond withmultiple identical responses to a given query. What is necessary fromthe messenger's point of view is to know the number of separate devices,i.e., separate UIDs, that responded to a particular query. Thus, everyone of the UIDs generating the responses to a particular query with aparticular timestamp such that they are associated with that particularquery will be logged, such that the messenger can now have a very clearand instant feedback as to the number of individuals actually payingattention to their particular advertisement. For example, if there were10,000 attendees at an event and 5,000 responded to a particular query,this would indicate to the messenger that their advertisement actuallywas viewed by 5,000 attendees. Without this system, it is nothing butspeculation as to how many of the attendees are actually viewing theadvertisement.

The program then proceeds to a function block 708 after registration inthe database to basic register a payment, as will be describedhereinbelow, to indicate that a new UID has been added to the system.The program then proceeds to the “Done” block 710.

Referring now to FIG. 7A, there is illustrated a flowchart depicting theoverall revenue model, which is initiated at a block 712 and thenproceeds to a block 714 to determine if the new UID has been created. Itshould be understood that it is possible for an individual 202 to inputthe wrong seat number and, as such, duplicating another seat number thatis already been entered into the system. If the UID is associated onlywith the seat number, there could be a possibility of a duplicate. If itis a new UID, the program proceeds to a function block 716 to determineif there is a duplicate in the database due to the input of a wrong seatnumber or such. This is the local database or the verification database.Program then proceeds to a decision block 718 to determine if it isunique and, if not, it rejects and, if so, it proceeds to a functionblock 720 to increment a payment counter. This payment counterinformation is stored in the verification database with a timestamp forthe particular increment, as indicated by a block 722, and then theprogram flows to a block 724 in order to accrue the value and into ablock 726 in order to transfer value.

Referring now to FIG. 8, there is illustrated a flowchart depicting theoperation at the MU 110, which is initiated at a block 802 and proceedsto a decision block 804 in order to determine if the application on theMU 110 has been initiated. If so, the program flows to a block 806 topresent the ticket input screen to the individual 202. The program thenflows to a decision block 808 to determine if the ticket information hasbeen input. As noted hereinabove, that input is basically the section,row, and seat information that is typically on the ticket. However, anyinformation that is unique to the ticket can be provided as inputinformation. One advantage, however, of having the actual “physical”location of an individual is in a situation where in a prize isdelivered to that individual as a result of some response. It may bethat query is to require the individual to continually “tap” theirresponse key at a rapid rate and for a long duration of time and theindividuals that exceed a particular threshold will be awarded, forexample, a T-shirt. This can then be delivered to their seat.

After the information on the ticket has been acknowledged as having beeninput, the program flows to a block 810 in order to create the unique ID(UID) on the device itself. This UID, as described hereinabove, isbasically the information regarding the section, row and seatinformation associated with the ticket, in one example. This is createdon the device and stored on the device as a local value. The programthen flows to a decision block 812 in order to determine if the nextstep, the requirement that a response be provided, is to be provided bya visual cue. The visual cue could be a sign at the gate that indicatesto the individual that they are to initiate their application on theirdevice and then depress “1” for an indication of the Male gender and,for indication of the Female gender, depress “2” when the display of thepotential or available response buttons is displayed to the individual.Of course, the display will only be displayed after the operation isinitiated. This is the process that is associated with the “Y” pathwhich flows to a function block 814 to generate external visual cue,either in real time or as a fixed display, and then the program flows tothe function block 816 to present the screen or display with the variouschoices on the user's device. If, alternatively, no visual cue isprovided externally, the user is presented on their device with a screenthat provides a choice with a query, such as “select your gender” withonly two choices provided, “1” for the gender Male and “2” for thegender Female, as indicated by block 818. Once user has selected one ofthese two, then the application will shift into the full response modeand a full-screen of all available responses will be displayed, as willbe described hereinbelow.

The program then proceeds to a decision block 822 to determine if theselection has occurred, and, if so, the program proceeds to a functionblock 822 in order to send the created UID and that the response code(RID) along with a timestamp to the server and in the program proceedsto Done block 824.

Referring now to FIG. 9, there is illustrated a flowchart depicting theoverall operation of the user entering the venue and the overalloperation. The program is initiated at a block 902 and then proceeds toa block 904 wherein the user enters the venue. Once the user enters avenue, the user then initiates the application, as indicated by block906, which, as described hereinabove, can be initiated by the user, orsome external device such as a scanner or gate beacon or the such can beutilized to automatically activate the application upon passing a gate.When the application is initiated, it will access the network anddetermine the SSID or some similar identification information associatedwith the network from the network, as indicated by block 908. Theapplication will present to the user a prompt for ticket information, asindicated by block 910. The program then flows to block 912 wherein theuser will key in the ticket information. However, alternatively, therecould be provided the ability of the user's device to actually scan theticket with the camera which will allow the camera to extract uniqueinformation there from. The unique information could be, in the onedisclosed embodiment, the section, row, and seat information associatedwith the ticket or could be some unique code on the ticket. As long asthis information is unique as to all other individuals bearing a ticket,this will facilitate the operation of the overall disclosed embodiments.Once the ticket information has been input, this allows the unique UIDto be created with that information. The program then flows to adecision block 914 in order to determine if there is a visual cue. Ifthere is a visual cue, the application will present the user with choicein block 916 providing choice buttons associated with a particularvisual cue that are necessary in order to respond to the visual cue. Ifno visual cue is presented externally, the program will flow to a block918 to present the user with a screen having both a query and the choicebuttons associated there with. Once the choice has been made, asindicated by a block 920, the program flows to a function block to 922in order to create the UID with a timestamp and then sends the UID andthe timestamp in association with the RID to the server, as indicated byblock 924. It should be noted that each available choice will have somecode associated there with. As will be noted here below, there are alimited number of available choice buttons that will be provided touser. These will typically be limited to 40. Thus, a five-bit code isall that is required in order to support this number of availablechoices. Thus, the RID will be a code from 1-40 in binary form. This isa relatively small amount of information to be provided in atransmission. The program will flow to a “Done” block 926.

Referring now to FIGS. 10A and 10B, there are illustrated flowchartsdepicting the operation of presence recognition operation fordetermining when a device, an MU 110, is passing through a gate.Referring specifically to FIG. 10A, the program is initiated at a block1002 and then proceeds to a block 1004 to run the application in thebackground. In this mode, the full application is not running but,rather, a background application that performs a “sniffing” operationfor known signals on one of the multiple radios that may exist withinthe device. For example, some devices will have a cellular transceiverinterfacing with the cell network, and 802.15.4 radio for interfacingwith Wi-Fi, a Bluetooth transmitter and maybe a Zigbee transmitter.Additionally, a thread transmitter may also be provided for interfacingwith these types of devices. This background application merely looksfor the presence of one of these transmitting devices external to thedevice in order to read its identifying information. This is unique tothat device and can be, through a lookup table locally on the device,utilized to take some action such as launching the full application.

The program proceeds to a function block 1006 in order to scan for, inthis example, a beacon. A beacon is typically at a transmitting devicethat not only has a unique ID but also transmits data along with itstransmission. This is a one-way transmission and does not require anytype of handshake in order to receive the information. Sometechnologies, such as Bluetooth, do require “hearing” in order toreceive information from the transmitting device. The program thenproceeds to a decision block 1008 in order to determine if any beaconinformation has been received. If so, the program flows to a decisionblock 1010 to determine if any information received from the beacon,such as a command, is a valid command which can be operated on by thebackground program or application. If not, the program flows back to theinput of function block 1006. If the command is valid, the program flowsto a function block 1011 in order to launch the full application andthen to a “Done” block 1012.

Referring now to FIG. 10B, there is illustrated a flowchart depictingthe use of a BLE transmitter. The BLE transmitter is a device that cannot only send a unique identifier but also transmit information withoutrequiring “pairing.” Program is initiated at a block 1014 and thenproceeds to a block 1016 in order to run a background application forthe sniffing operation. The program then flows to a function block 1018in order to scan for BLE codes, i.e., the unique identifier. The programflows to a decision block 1020 to determine if such has been receivedand, if not, back to the input of function block 1018. Once received,the program flows to a function block 1022 in order to lookup the codelocally. If the code, stored in a local database, is valid, thisindicates, via a decision block 1024, that the code is a recognizablecode, i.e., one that is associated with the overall operation of thesystem. If so, the program flows to a function block 1026 in order tolaunch the full application and then to a “Done” block 1028.

With the automatic recognition of an external transmitter with a smalllocal transmission range disposed at an entrance gate, all that isrequired for an application to be launched is just a recognition of thepresence of a particular device within the transmission range of abeacon or similar type transmitting device. This, of course, onlyinitiates the application. There is still a requirement that theindividual viewing the screen, which is typically achieved by some typeof audible tone or prompt, is to provide some type of response. As notedhereinabove, that response may be a response to a query actually outputby the device, which indicates that at least the individual is lookingat their phone and interfacing with the application. It could be thatthe response is in response to viewing some type of visual cue local tothe gate. This visual cue could be a “fixed” visual cue or it could be atime varying visual cue. With a time varying visual cue, the timescalethat is provided on the response that is sent can be utilized to verifythat this response was activated at the gate as opposed to somewhereelse. Of course, that necessitates that, not only does the unique IDhave a timestamp associated with it at the time it was created, but alsothat the response to the visual cue be timestamped.

Referring now to FIG. 11, there is illustrated a diagrammatic view ofthe initial screen display to the user of the MU 110. As describedabove, there is provided a ticket 112 that has associated therewithmultiple fields. There is provided with some type of unique barcode1102, information about the event 1104 and also the seat and rowinformation in a field of 1106. In the disclosed embodiment, thesection, row and seat information is what is input. The screen providedis represented by a reference numeral 1110 and displays a text prompt tothe user to enter the information regarding the section, the row and theseat in fields 1112, 1114 and 1116, respectively.

Once the user enters this and selects a “Confirm” field 1118, then thisinformation is utilized to create the unique ID as describedhereinabove. Then one of two events will happen. The first is that ascreen 1120 will be displayed that basically provides a query requestingthe selection of one of two choices, in this example, either a Male or aFemale gender. By selecting one of these two, a response can begenerated that actually provides information to the database as to thegender of the individual. Interestingly enough, as will be describedhereinbelow, this provides to the messengers information regarding thegender of each unique ID (UID) that is in the system. However, studiessuggest that a certain percentage of the individuals will make a mistakeon their entry for whatever reason in a certain number of individualswill actually put the wrong answer in. Thus, what will be indicated tothe messengers is that statistically this person is one gender or theother, but this is not a 100% indication.

The other aspect of it will be the presentation of a screen 1122 whichprompts the user to view some type of screen that is proximate to theentrance gate. The screen is utilized for the purpose of providing thefirst query which is required in order to actually create the entry intothe database of the UID for that particular device. There is presentedin this screen 1122 various response fields, in this example, 3 responsefields, 1124, 1126 and 1128. In this example, there would be provided aviewable screen that provides some type of query requiring the selectionof one of three selections as the response. These responses, in additionto allowing registration of the UID in the database, also provide somestatistical information about a person associated with that UID.

Referring now to FIG. 11B, there is illustrated a depiction of an actualscreen that is provided after registration of the UID for providingresponses to various queries. This screen is represented by referencenumeral 1130. This screen 1130 provides a fixed number of displayedresponse codes. There are provided a first column 1132 of outputalphabetical characters, the first 10 characters of the alphabet from Athrough J. There is provided a second column 1134 for the first tennumerical characters from 1 through 10. There is provided in a thirdcolumn 1136 the first 10 the primary colors, each color represented in acircular button. There are provided in a fourth column 1138 ten basicshapes such as a square, a circle, a triangle, etc. Thus, there areprovided 40 fixed characters that will always be provided on the screen.None of these characters is dedicated to any particular response to anyparticular character. When building a query, designer of that queryactually maps a particular response key to the database and thedefinition of a desired response, as will be described hereinbelow. Allthat is necessary is to provide a simple code for each one of thesebuttons. Thus, only a five-bit code is required to provide the code foreach of the buttons. For example, it may be that the first query has tworesponses that are presented, “A” and “B.” In the database, it may bethat this particular query determines that the people answering thequery with a “A” response have a likelihood of being 60% Male and thepeople answering the query with a “B” response have a likelihood ofbeing 60% Female. First, the fact that they answered with eitherresponse indicates that there looking at the screen and this isimportant information to have. A further refinement of the response canbe provided by mapping a particular response to certain statisticalrecords. This will be described in more detail herein below.

There are also provided three response buttons 1140, 1142 and 1144,respectively, that are not responses that can be mapped into thedatabase outside of the MU 110. These buttons 1140-1144 are provided foranother function, and the function is to allow interface with theinternal application in response to a visual cue, which will bedescribed hereinbelow.

Referring now to FIG. 12, there is illustrated a flowchart depicting theoperation of running a query at a top level. This program is initiatedat a block 1202 and proceeds to a block 1204 in order to run the audioor visual prompt. The program flows to a function block 1206 in order toset the time window within which a response is to be received for thatparticular query. The program then flows to a decision block 1208 todetermine if any responses have been received and, if so, then to afunction block 1210 in order to populate the database with a response,which just indicates that this particular MU 110 via its UID is actuallyassociated with a person looking at the prompt. The program then flowsto a decision block 1212 to determine if the time window has closed forreceiving responses. If not, the program will continue to loop back tothe input of the decision block 1208 until the time window is closed forthat particular query, at which time the operation is terminated at a“Done” block 1214.

Referring now to FIG. 13, there is illustrated a diagrammatic view ofthe various data structures generated by the MU 110 during registrationand operation. In a first data structure 1302, there is illustrated thevarious data fields for the UID. They are defined as a first field 1304associated with the section, a second field 1306 associated with the rowand a third field 1308 associated with the seat. A fourth section 1310,an optional section, is associated with a timestamp that can be utilizedat the time of the creation of the UID to uniquely define it in theevent that somebody else actually this enters their seat number, forexample. This is not a timestamp that is used for identification of thetime at which the UID is transmitted but, rather, just additionalinformation to make the UID more unique. Of course, it could also beutilized for the purpose of determining the time in which the UID wascreated. This particular data structure requires very little databandwidth to transmit such, as the information contained in there isminimal.

For the second data structure, a data structure 1314 is provided for thebutton code for the response, which, as noted above, is the response ID(RID). This is a five bit code. The actual response that is sent isillustrated by a data structure 1316, which is comprised of a first datafield 1318 having associated therewith the UID, a second data field 1320associated with the RID, a third data field 1322 associated with atimestamp, TS. This field 1322 is actually the timestamp that isgenerated when the response is actually created as compared to thetimestamp infield 1310 that further defines the UID as unique. Overall,this response data structure 1316 is all that is required to betransmitted in response to seeing a visual cue. There is no two-waycommunication that is required between the server and the MU 110, thusreducing the overhead load on the network traffic. Thus, for example, ifthe response data structure 1316 required three bytes of data, 10,000participants viewing a visual cue and responding thereto would onlytransmit 30 Kbytes data within the window. If that window defined by thequery was open for just one second, there would be required a minimumbandwidth of 30 Kbytes/sec, which is well below the lowest bandwidthWi-Fi connection to any network. Thus, if one of the responses was tosee how many times any individual associated with a UID could “tap” aparticular response button, it would still be difficult, with the humanresponse time, to exceed any practical bandwidth limit in a network. Itis a minimization of overhead and the production of the actual data thatis required to provide information to an messenger. Again, what isprovided by the response button is both an indication of “eyes on thescreen” and also some back end statistical data.

Referring now to FIG. 14, there is illustrated a flowchart for theserver receiving the response, which is initiated at a block 1402 andproceeds to decision block 1404 in order to determine if a response hasbeen received. When received, the program flows to a function block 1406in order to resolve the particular response. What has been received atthis point is a response having a UID, and an RID and a timestamp. Whatis resolved is, knowing the time window, the presence of a unique codewhich is a combination of the RID and QID (query ID), is indicated byfunction block 1408. This combination, as will be described hereinbelow,is a unique ID that can be utilized for back end statistical analysis.The UID is also resolved and is utilized to indicate that a particularUID has responded (noting that any time that response is referred to asbeing responded by UID, this also means that it is being responded by MU110). If the query, for example, just wanted to know how manyindividuals are looking at the screen in response to a particular query,any response received, whether it be multiple responses or a singleresponse, during the time window associated with the query will providean indication, for for all received UIDs, of the number of individualsthat paid attention to the query, and all that is required to resolvethis particular query into any useful information is the UID. By lookingat the combination of the unique RID plus QID, further information canbe determined to resolution associated with other tables mapped to thisparticular query and response. The program will then flow to a functionblock 1410 in order to update various tables and into and “End” block1412.

Referring now to FIGS. 15-18, there are illustrated diagrammatic viewsof how the tables are generated for various combinations of RIDs for aparticular query ID (QID). For example, for a given query, there may bethree responses provided, R(1), R (2) and R(3). It may be that the querypresented to the individual is the choice of responses “A,” “B,” and“C.” These particular response codes will be mapped to some type ofinformation associated with that response. For that response, i.e., forthe first response in association with a particular QID, QID+R (1), thiscombination being a unique ID that defines a unique object or tablewithin the database for this combination. Thus, within this particulartable associated with that unique ID, the particular UIDs that respondedas such can be contained therein and each of these UIDs will providepointer back to the actual UID record associated with that UID. FIG. 15illustrates these particular tables.

In FIG. 16, there is illustrated a further refinement of theinformation. As will be described hereinbelow, queries can be providedwith information associated with classifications. For example, there maybe a classification of “gender.” This would have the sub classification,at its highest level, of male or female. Classification would have aclassification ID of CID and the sub classification would have a uniqueID of SCID. For example, take the example of gender. This can be soclassified into possibly ten different analytical “bins.” The systemcould be designed such that a prior knowledge of a particular generatedquery could be resolved into ten different percentage classifications,one wherein the gender is classified as follows:

10% F/90% M

20% F/80% M

30% F/70% M

40% F/60% M

50% F/50% M

60% F/40% M

70% F/30% M

80% F/20% M

90% F/10% M

Thus, a particular response can actually be mapped to one of thesestatistical bins. This would thus require that the designer of the queryunderstand that when a particular individual responds with theparticular response, this will indicate to the database that, forexample, 80% of the respondents are female. Each of these particular subclassifications can be mapped all the way back to the UID and theQID+RID unique code. This is illustrated in FIG. 17. The actual CID isillustrated in FIG. 18, indicating that there can be one CID for gender,one for political affiliations, one for ethnicity, etc. By utilizingprior information known to the designer of the query, each response canbe mapped to multiple different classifications and sub classifications,such that just the response provided by any MU 110 can be resolved intoinformation regarding the particular individual that responded to such.Certain information can be determined as to their gender, as to thepolitical affiliation or as to their ethnicity and other suchinformation.

Referring now to FIG. 19, there is illustrated an additionaldiagrammatic view of how the mapping occurs. The additional responsedata structure 1316 is resolved such that the UID will define a UID datarecord or object that is to be updated with the various informationprovided by the analysis and the mapping of the responses. The resolvingtechnique defines, with the response and the query in which the responsewas received, the unique ID for the QID+RID denoted in a table or objectassociated therewith. This is a table 1902, which is updated with theparticular UID that responded with that particular response code. ThisQID+RID unique code is mapped to a CID table 1904 to place a pointer tothe UID therein. This also points to an SCID table 1906 such that theUID can be placed therein. There's additionally a QID table 1908 thathas the particular UID associated with this response placed therein.Thus, by looking at any one of the tables associated with the UID table,all of the UIDs that were associated with a particular response for theQID will have a reflection of the number of UIDs that responded as such.If, for example, one wanted to know how many UIDs responded to just thegender question where either response indicates some information aboutgender, one need only look at the CID table 1904 associated with thatparticular response, i.e., the one to which the QID+RID unique code wasmapped to. If one wanted to look at how many respondents replied to theparticular query, all that is required is to look at the QID table 1908and this will give a total of all of the UIDs that responded to thequery. There is knowledge, of course, as to how many total UIDs are inthe system or are present at the event. If, for example, at halftime ofa basketball game, any query was presented to the attendees and aresponse resulted in a 40% response, this would indicate to themessengers that 40% of the attendees were viewing the screen. Someinformation can be gleaned from this information. However, this providesan actual real time indication to the messengers of the fact that theywere able to have 40% of the attendees with “eyes on the screen.”

Partly referring now to FIG. 20, there is illustrated a more detaileddiagrammatic view of the mapping operation. It can be seen that, foreach UID in table 1910 that each UID has associated therewith a QID, anSCID, a CID and a QID+RID. There is shown the mapping to the table 1902which shows multiple UIDs mapped thereto. There are illustrated two UIDtables flanking 1910 Thereto. These tables also mapped to a secondQID+RID table 1902.

Referring now to FIG. 21, there is illustrated a diagrammatic view of anoverall template for creating a query. This template provides theability to map any particular classification and sub classification withany response. For example, illustrated in the template are fourclassifications, gender, nationality, ethnicity and political bias. Eachof these may be selected as somehow associated with a particular query.The designer of the query will then be provided with the ability ofproviding any number of response buttons in their queries. Each of theseresponse buttons just needs to be mapped to a particular subclassification ID, SCID, in order to give it meaning. Thus, the genderhas a CID, to which the particular query is mapped for a particularquery. This will be CID gender ID. There may be, as noted hereinabove,nine sub classifications. Each of the response buttons can be mapped toone of the sub classifications. It is illustrated that the response R1ID is associated with the RID to the particular map button, for example,the “A” button. This will be mapped to the particular unique QID+RIDcode for that combination of the particular QID for the query beingdesigned and the RID associated with the “A” button. This will begenerated as a particular object in the system or record for that uniqueID. This would be mapped to a particular SCID. It is asserted to keep inmind that this particular SCID is a predefined SCID, such that it can beutilized to collect data from multiple queries. It is not associatedwith just this particular query only but the particular QID isassociated with query and the particular mapping of the RID to aparticular button for that query in the form of the unique ID, QID+RID.It is noted that the first RID, that associated with the R1 IDassociated with the “A” button will exist for each of the particularclassifications, i.e., for the nationality CID, the ethnicity CID andthe political bias CID. Thus, what will happen is that, upon providing aresponse via the “A” button for that particular query, this particularUID will be mapped into each of the SCIDs to which that response buttonis mapped. By looking at the QID table, the total number of UIDsresponding thereto will be known. By looking at an SCID table, all ofthe responses over all queries will be known. With respect to gender,for example, if there were nine different bins associated with ninedifferent SCIDs, a bell curve could be generated from all of the datathat is received for the multiple queries indicating the general gendermakeup of the crowd. This is all derived from just simple responsesreceived from multiple MUs 110 transmitting a minimal amount of dataresponses for a query to a server.

Referring back again to FIG. 11B, there are provided 40 fixed charactersthat will always be provided on the screen. All that is necessary is toprovide a simple code for each one of these buttons. Thus, only afive-bit code is required to provide the code for each of the buttons.For example, it may be that the first query has two responses that arepresented, “A” and “B.” In the database, it may be that this particularquery determines that the people answering the query with a “A” responsehave a likelihood of being 60% Male and the people answering the querywith a “B” response have a likelihood of being 60% Female. A furtherrefinement of the response can be provided by mapping a particularresponse to certain statistical records. Each of the responses may havestatistical information associated therewith. In this way, each responsemay have an SCID associated with that particular response in the giventime window. Again, the “A” response may have associated with it thegender SCID corresponding to a 0.6/0.4 Male/Female percentageclassification. This SCID association with the response may only lastfor the duration of a particular live event, and may only be relevant tothat live event. So, at a live event on a different date, any SCIDsassociated with the responses may be newly formed at that particularlive event and are thus unique to that particular live event. However,it will be understood that statistical information may be carried overfrom other live events if the desired statistical data warranted such.

It may also be that the statistical data associated with responses canbe shared across a series of concurrent live events. For instance, theSCID corresponding to a 0.6/0.4 Male/Female percentage classification inassociation with the “A” response may be shared across four differentlive events taking place on the same night and around the same time. Inthis embodiment, it may be that the four events all ask a particularquestion at the same time based on a pre-defined time window for thequestion. This question asked at all four of the events may also havethe “A” response as having the same SCID associated with it so that thedifferent audiences can be evaluated using the same statisticalassociations. This may be useful when polling audiences at differenttypes of events. If an “A” response is expected to be answered 60% bymales, it can be measured against differing audiences in order todetermine if that assumption holds true, taking some variation intoaccount. For instance, if the question is asked at a sporting event and58% of the responses are from males, but when asked at a concert the “A”response is chosen by a population of only 35% males, this providesuseful information for particular types of venues.

The visual cue 108 may provide for any arrangement of any of the 40characters. In some embodiments, the characters used might be in alogical order (“1-2-3”, “A-B-C”, etc.), or they might be arranged in arandom, or seemingly random, order. In some embodiments, the responsesdisplayed on the visual cue 108 may only be used once at a particularlive event. For instance, at a live event, if the particular questionsasked have used all characters from 1-10 and A-J, it may start usingcolor or symbols exclusively, as 1-10 and A-J have already been usedearlier in the event. This allows for certain statistical data to beassociated only with particular response characters. However, in otherembodiments, a particular question may require certain characters forthat question, and thus those characters would always appear with thatquestion.

As provided herein, the 40 fixed characters on the mobile unit screenallows for users of the mobile unit to simply press or tap theparticular character that is associated with their desired response.Doing so will cause the response to be transmitted to the local centraloffice 120, along with the timestamp and other information to betransmitted. This thus allows for a one-click user experience. In someembodiments, from the user's perspective, users will only see thecharacters presented. No text or other information pertaining to thequery being displayed on the display cue 108 would be displayed on themobile units. Thus, each of the mobile units acts as a response systemonly, encompassing only that aspect of the system. The mobile unitresponse system would not perform any other function besides accepting auser's response and transmitting that response to the local centraloffice 120.

Referring now to FIG. 22, there is illustrated a flowchart of oneembodiment of a process for tracking attendee participation at a liveevent 2200. The process begins at step 2202 when an attendee or userpasses through the gate or launches the application on the attendee'smobile unit. The application may be a standalone application, or part ofanother application's functionality. If part of another application'sfunctionality, such as an official Major League Baseball app, there maybe an extra section or tab of the application that the attendee mayselect to begin using the functionality provided for by the presentinvention. At step 2204, the attendee follows steps to perform theinitial setup and ID creation process as provided herein. This may occurwhen a user answers an initial query, in addition to providing theuser's seat number. This provides for an indication that the attendee isready and willing to participate in the polling, games, or otheractivities provided at the venue 102.

At step 2206, upon completion of the initial setup and ID creationprocess at step 2204, the participation of the attendee is logged. Tokeep track of the number of attendee's that have chosen to participate,a digital counter may be incremented as well to indicate that anotherattendee has used the application to participate. In some embodiments, alog might not be kept and only a digital counter used, or in otherembodiments only a log would be kept, as desired by the partiesinvolved. The log and/or counter may be saved either permanently or onlyinitially by the local central office 120, and the data would be latertransferred to the central remote office 126. This provides a uniqueadvantage over more traditional polling, statistics, or advertisingsystems because the present invention allows for a verified viewer. Intraditional methods, such as an ad banner or ad video at a live event,for example, it is unknown how many people paid attention to the ad. Thepresent invention allows for an exact number of verified viewers orparticipators to be tracked.

Upon completion of the initial setup and ID creation process and step2206, at step 2208, a service provider is paid a flat fee for the use ofthe service. The service provider may be the owner of the central remoteoffice 126, the developer of the application, either developing thestandalone app or developing the functionality for the official partnerapp, or any combination thereof. The service provider would receive theflat fee as compensation for providing the services described herein tothe venue 102. The flat fee may be for any value. In some embodiments,the fee may be a low value, such as $5.00, which may be low enough toattract live event venue customers or live entertainment organizationsto use the service, while allowing for the revenue stream for theservice provider to be substantial if there is high attendeeparticipation. The flat fee would be charged to the live event venue orlive entertainment organization for each attendee at a live event thatcompletes step 2204. Preferably, the flat fee would only be paid onceper live event per attendee that completes step 2204. This means thatthe fee would not be paid again when an attendee who has alreadycompleted step 2204 participates again during the same live event byanswering other polls, participates in games, or other activities.However, other embodiments may allow for a fee to be charged each timean attendee participates at a live event. For example, if 20 pollingquestions are asked at a live event, the flat fee would be charged forthe same attendee 20 times if that attendee participates in everyquestion.

Referring now to FIG. 23, there is illustrated a diagrammatic view of anoperation of the current system for facilitating the purchase ofmerchandise from outside vendors outside of the overallpolling/statistical collection system disclosed hereinabove. As notedhereinabove, the Mobile Unit 110 can be any type of device forfacilitating communication with the hotspot 116 or wireless interface116. However, in most cases, the MU 110 is a smart phone that isregistered with a provider that provides a data plan for that particulardevice. This utilizes a separate radio on a separate antenna 2302 forinterfacing with a cell tower 2304 or any other type of data interface.This cell tower is interfaced with a service provider 2306, with whichthe individual 202 and the device 110 is a registered. Thus, the serviceprovider 2306 will have, due to the fact that the MU 110 is registeredwith the service provider 2306, information regarding payment optionsuch as credit card and address information with respect to the actualindividual 202, in addition to other information about the individual202. As noted hereinabove, the local CO 120 has no information regardingthe MU 110 other than what is provided via inputting the ticketinformation and running the application. No information is provided withrespect to unique information about the particular MU 110 and none isnecessary.

In this particular embodiment, the screen 108 displays information tothe individual 202 which, for polling operation, elicits some type ofresponse from the individual 202 via the MU 110. In this embodiment,however, what is displayed to the individual 202 for viewing is an offerfor merchandise from an outside vendor, the definition of outside vendormeaning a vendor or a party that is outside of current system, i.e., itis not associated with the local CO 120. The vendor could be a merchantthat sold goods online or actually be a merchant that was within theconfines of the local event within the venue 102. The user is promptedto use one of selection buttons 1140-1144, of which selection buttons1140 is illustrated in FIG. 23 on the device screen of MU 110. Theadvertisement might state “the following goods offered for sale at asignificant discount of X %-want to take advantage of this offer, pleaseselect S1 within the next 10 minutes on your FEVR application.” The useror individual 202 would then have 10 minutes, in this example, withinwhich to make the selection. The selection merely requires them to pressthe button 1140. What would that happen is that a second screen 2308would be presented on the MU 110 to the individual 202 to complete thetransaction external of the local CO 120 and any collection of data orstatistics. Everything after this point is unknown to the local CO 120and whoever controls such.

With this offer screen 2308, the data link is routed through to theservice provider and then to the Internet 2310 to provide a link to avendor 2312. However, the entire offer could be facilitated internal tothe MU 110. The primary issue is that information is required to beinput to the offer screen 2308 in order to provide information, ifneeded. It could be that only a single items is being offered with norequirement to input a size or color, etc. In this case, all that isnecessary is to push the button 1140 and the transaction is completedwith the vendor 2312. However, if additional information is required,the offer screen 2208 allows the individual 202 to interface with thevendor 2312 in order to provide such information. What then occurs isthat service provider interfaces with the vendor 2312 in accordance witha prearranged transactional agreement to actually bill the individual202 through their contractual relationship. This can be facilitated dueto the fact that the service provider 2306 has all the informationnecessary to just provide this payment on the individual's monthlystatement.

Although the local CO 120 could provide this query/advertisement/offerfrom the local database, it also could be retrieved from a contentprovider 2316 via the Internet 2310 when needed. This allows the contentprovider 2316, in one example, to actually live stream theadvertisement. The main point is that the advertisement is, 1) presentedto the individual 202 with predetermined selection button associatedthere with and, 2) that a time window is provided.

Referring now to FIG. 24, there is illustrated by diagrammatic view ofthe overall system flow for facilitating the presentation of the offerand the completion of the transaction, including the billing. A block2402 represents the generation of the offer which is sent to the display108 with a selection field 2404 displayed that their one. The MU 110displays the advertising addition to the selection buttons 1140, in thisexample, which is then, in accordance with the above noted description,sent to provider 2306. All that is really necessary to send to theprovider is, at the minimum, the selection button in addition to sometype of location information. With this information, it is possible toallow the service provider or even the vendor to determine whichpromotional advertisement is associated with and also with you venue 102it is associated with. To facilitate this, some kind of locationinformation is provided via a block 2408. Since there is no two-waycommunication with the local CO 120, the information that the MU 110 hasis GPS information, SSID information of the wireless hotspots, and aticket number. The ticket number, if it is in the form of a seat number,section number, and room number, may not be unique to any particularsystem, as multiple venues might share the same seat number. However, itmay be that there is a unique ticket number that is unique across allvenues, and this may actually define the location. If not, then GPSinformation could be provided to define the location of the MU 110 atthe time of the generated response. This provides the actual physicallocation of the MU 110, and this just needs to be correlated with aknown physical location of a particular venue. Thus, this promotionaladvertisement or offer might be displayed at multiple venues throughoutthe country. For example, on any given weekend, there may be multiplefootball games sponsored by the NFL®, and the promotional offer oradvertisement, since it may only be presented during timeouts, halftimeevents, etc., would not be synchronized across multiple games andmultiple channels. In this event, the vendor would have to know thephysical location of the event in order to determine which venue thiswas associated with in order to verify information. Additionally, aparticular vendor or program operating outside of the local CO 120 wouldhave to be aware of the time in which it was presented. Thus, the localCO 120 would have to inform the vendor of the time in which thepromotional advertisement was actually displayed, and this could becoordinated with the generation of this response via the button 1140.

Thus, the service provider 2306 would receive both the informationrecording the response that was sent, i.e., an indication that thebutton S1 was pressed, the location information, and also the timeinformation in the form of a timestamp. This information is then routedto the vendor 2312. The vendor 2312 has a contractual relationship withthe service provider 2306 in order to allow the service provider 2206 tobill the individual 202 through the contractual relationship it hastherewith. This is represented by a block 2420.

Referring now to FIG. 25, there is illustrated a flowchart for theoverall operation of the merchandising feature area. This is initiatedat a block 2502 and then proceeds to a block 2504 or in order togenerate the offer. This requires the definition of a time window, asindicated by block 2506. This is then displayed, as indicated by a block2508, with the appropriate response button that must be selected inorder to take advantage of this particular promotional offer. Theprogram then flows to block 2510 wherein the individual 202 views theoffer, and a selection is made, as indicated by block 2512. At thispoint, a button ID with a timestamp is created, this button ID not beingfor the purpose of the polling or statistical operation of the local CO120. It is merely provided in order to encode within the button ID theinformation about the button unique to the application, i.e., in thiscase it would encode the underlying code for the button 1140. This isthen combined with a timestamp and sent via the overriding offerapplication, which is associated with the screen 2308, to the provider2306 in order to interface with the vendor 2312 or to just send theinformation to vendor 2312. This is indicated at a block 2514. Theprogram then flows to block 2516, wherein this is sent via the MU 110 tothe carrier and then to the 2312. At this time, a determination must bemade as to whether a timestamp associated there with, in addition tolocation information, indicates that the selection was made within thetime window, this indicated by decision block 2518. If not, this isrejected. If so, then the overall order is processed, as indicated by afunction block 2520. This process includes verifying payment and thensending order/payment to the vendor, as indicated by block 2522. Thevendor will then deliver the product as indicated by block 2524, and thecustomer is billed by the provider, as indicated by block 2526. Theprogram then proceeds to a Done block 2528.

Referring now to FIG. 26, there is illustrated a flowchart depicting theoperation of ensuring that only a single UID is associated with a singleseat. The reason for this is that, from an integrity standpoint, anadvertiser wants to have statistics that cannot be manipulated. Oneissue might be that another entity could manipulate the statistics byentering other UIDs into the system due to the fact that the timestampmakes it more unique. By requiring each UID to have a unique seatnumber, the situation wherein a second application has the wrong seatnumber entered therein will result in a rejection. Even if first UIDassigned to that seat number is incorrect, UID will be rejected when theindividual 202 with the correct ticket numbers tries to enter in theirseat number. Since it had already been incorrectly entered and a UIDregistered with that seat number, the new one will be rejected. Thiswill be an error, but it will be tolerated. The important thing is thatan advertiser or somebody interested in statistics about the crowd canbe ensured that, for a venue having associated therewith 10,000 tickets,there will be no more than 10,000 UIDs generated. Even if only 8000attendees registered their UIDs, the advertiser can be assured thatthese are in fact associated with the “eyes” that they desire to bedirected at their particular advertisements, etc.

The program is initiated at a block 2602 and then proceeds to decisionblock 2604 to determine if there is a new presence, i.e., a new MU 110is entered at the event boundaries, and a ticket has been utilized inorder to enter the unique information from the ticket and create theUID. UID is sent to the system, the local CO 120 and compared to thealready existing UIDs, as indicated by block 2606. The program thenflows to a decision block 2608 to determine if this newly received UIDis unique to the system. If not, this indicates that it is a duplicatefor some reason, and it is rejected. If so, the program flows to afunction block 2610 New Record, as described hereinabove and then to ablock 2612 in order to register payment, as described hereinabove.

FIG. 27 illustrates a flowchart depicting the overall operation ofcollecting statistics via all of the responses received in thecrowd-based response system, which is initiated at a block 2702. Theprogram then flows to a function block 2704 in order to generate thequery. As noted hereinabove, the query is actually designed such that ithas embedded therein statistical information that can be derived from aparticular response. For example, press “1” for female and “2” for male.This particular key, i.e., that for the “1,” is for that query duringthat time and is statistically related to the gender female. Of course,studies of individuals responding to that question may indicate that the80% will actually be female. Thus, a statistical certainty of 80% can beassociated with that particular “1” button for that particular queryduring that particular time window. That will accordingly be mapped toan SCID for that particular statistical certainty of female. It may beat another query was designed such that the button “C” was mapped tothat SCID and, for that query in that time window, the button “C” isassociated with the statistical certainty of 80% female. Aftergeneration of the query, the various UIDs responding thereto, asindicated by a block 2705, will be collected and the various datarecords updated. As noted hereinabove, a particular response can beassociated with multiple SCIDs for a given button.

After all of the UIDs are collected and mapped to QIDs, CIDs, and SCIDs,the program flows to a decision block 2708 in order to determine ifanother query is in the queue. If so, the program backs around to theinput of the block 2708, and, if not, the program flows to a block 2710indicating that this is a last query.

Referring now to FIG. 28, there is illustrated a diagrammatic view ofhow a query is mapped to a QID, for example. When the query is output,as noted hereinabove, a time window is defined which is uniquelyassociated with that query. The response is resolved down to the UID,the timestamp and the RID. In the QID, all of the UIDs responding willbe collected. Thus, all that is necessary is to look at the QID, whichis unique as to a time window. For example, if there were two queriesthat were basically identical, and they were generated at differenttimes, they would actually have a different QID, as each is unique withrespect to its time window. It may be that a particular individualassociated with an MU 110 presses the button more than once. This wouldprovide the same UID in the particular QID record more than once. Duringthe analysis, this can be discriminated. If it was desirable to see howmany unique UIDs responded to a particular query to see how manypeople's “eyes on the screen” there were, then all that would benecessary was to determine the number of unique UIDs that responded. If,on the other hand, it was desirable to see how many times a response wasprovided to a particular query, the QID for that query be analyzed forthe total responses including multiple responses from associated UID.This is illustrated in FIG. 23 in the form of two different queriesassociated with two different QIDs.

Referring now to FIG. 29, there is illustrated a diagrammatic view of anexample of an analysis with respect to these queries that are presentedin each query. They can be seen from this graph that initially, thefirst query had a low number of responses and, as the event wore on,certain queries have higher level of responses as opposed to otherqueries. These are total responses to a particular given query. Thefirst bar chart illustrates the total number of responses for a givenquery. An additional analysis can accumulate the total number ofresponses by accumulating UIDs over the time of the event. Additionally,an analysis can be performed to determine the actual participating UIDs.It may be that certain MUs 110 participate in the response-basedoperation more than others. There could be, for example, 30,000attendees, of which 10,000 are registered with an associated UID. Byknowing the number of total registered MUs 110, a determination can bemade as to what percentage at any given time is actually participating,and also an analysis can be made as to the distribution of participationby the registered users. There may be a certain portion that responds toevery query, a certain portion that only responds to 50% of queries, acertain portion that responds only 25%, and a portion that neverresponds. This can be important information for an advertiser/promoter.Additionally, since the seat number is known from the UID itself, asthis is embedded information therein, it is possible for the system toactually map responses to certain areas of the live event. For example,suppose that the event were a baseball game. It is well-known that seatsbehind home plate are the most expensive seats, and the bleachers arethe least expensive seats. It may be that certain queries are respondedto more heavily by attendees in the bleacher seats as opposed to thosein the behind home plate seats. A statistical certainty may actually beplaced upon a particular seat with respect to income, for example. Thus,if it is determined that at certain times during the event that moreresponses are being received from UIDs associated with behind home plateseats, it is possible to actually tailor the queries during those timesfor those particular attendees. The analysis can be performed real-timeto actually change the subject matter of the queries that are presented.

Referring now to FIG. 30, there is illustrated a simplified diagram ofthe mapping of a query to an SCID. Each query, the QID and the CID, canbe mapped to a particular SCID. This is defined in the design of theparticular query. The SCID is associated with a statistical certaintysuch that any choice can be associated with any SCID. As notedhereinabove, a query is defined with anywhere from one to multiplechoices, each choice associated with a particular button. That buttonwill be associated with one or more SCIDs. These, again, arepredetermined statistical certainties that are defined in the context ofthe particular query. Illustrated are multiple queries. The first query,query 1, has two choices, a choice 3002 and a choice 3003, meaning thatthere are two buttons associated with that query to allow the user tomake two choices. It is noted that a choice may also require thepressing of multiple buttons and not just a single button. Thus, thecombination of buttons would constitute a choice. The first choice 3002is associated with an SCID 3006, an SCID 3008 and an SCID 3010. Each ofthese SIDs 3006-3010 have a different statistical certainty associatedwith a different classification or CID, such as gender, politicalaffiliation, ethnicity, etc. The second choice 3003 is associated withan SID 3012, this being a different statistical certainty for adifferent classification. There is provided a second query, query 2,that is illustrated as having a choice, a single choice, that will beassociated with the SCID 3006, the SCID 3010 and the SCID 3012. A lastquery, query (m), has three separate choices, one associated with SCID3006, one associated with SCID 3008, and one associated with SCID 3012.This association is, again, defined in the design and the generation ofthe query. By having some knowledge of the particular query in thecontext thereof, a designer of the query can determine statisticalrelationships between that question, the response elicited and thestatistical certainty from that response. Again, the example of justselecting one of two choices, one for female and one for male, will beeasy to design, as it will be provided an SCID for male and an SCID forfemale. If there were a query asking if you are an out-of-town visitor,that would be a statistical certainty for a nonresident. A query forinformation regarding “your country of origin” could provide fiveresponses via five separate choice buttons for Europe, Asia, SouthAmerica or Canada, and these four choices would provide a statisticalcertainty for four different SCIDs, each associated with one of thosechoices. In this query, for example, each button that is provided atthat time for that query has a defined statistical relationship as aresult of being associated with a particular SCID, that statisticalrelationship defined by the properties of that associated SCID.

Referring now to FIG. 31, there is illustrated a diagrammatic view ofone example of the design of a collection of statistical data. In thisexample, there are provided two CIDs 3102 and 3104, the CID 3102associated with gender, and the CID 3104 associated with politicalaffiliation. There are associated with the CID 3102 three differentSCIDs, one for a ratio of 20% female/80% male, one for 50% female/50%male, and one for a ratio 80% female and 20% female. The CID 3104 isassociated with three SCIDs, one for 20% Democrat/80% Republican, 50%Democrat/50% Republican, and 80% Democrat/20% Republican. The designercan define for a particular query which statistical relationship isapplicable and select the closest SCID that has embedded therein thestatistical certainty. Thereafter, for any given query, each query ornumber of queries can be associated with that particular SCID. Forexample, there is a group 3108 of QIDs that are associated with the SCIDhaving a ratio of 20% female/80% male. There is another group of QIDs3110 having in Association with the SCID for 80% Democrat and 20%Republican. It may be that certain QID in group 3108 are also QID ingroup 3110. Just the mere response to the QID and, of course, theparticular response button associated therewith, it being noted thatthere may only be a single response, will result in UID making thatresponse being populated into the record for a particular SCID. Whenanalyzing a particular SCID over multiple queries, a determination canbe made as to how many unique UIDs responded thereto or the total numberof responses. This of course must account for multiple taps and thesuch. This can be handled in the software response for any query suchthat not all responses from a single MU 110 are recorded-only a singlerecording of a response during a given time window will be recorded.

Referring now to FIG. 32, there is illustrated a diagrammatic view ofthe analysis of one group of SCID associated with the gender CID 3202.There are provided eight SCIDs associated with the gender CID 3202,ranging from 0.1/0.9 through 0.9/0.1 as a ratio of female/male. This isa binning process wherein UIDs responding thereto will be binned thereinfor all queries. Of course, each SCID is mapped to a particular QID suchthat any QID can be analyzed for the particular SCIDs that areassociated therewith. In the chart, it is illustrated that adistribution of binned UIDs are illustrated, it being noted that thereare more females than males in the overall responders. This graph doesnot show or illustrate the number of queries that were responded to;rather, it illustrates the binning operation of the actual responsesthat, through the design of the queries.

Referring now to FIG. 33, there is illustrated a flowchart depicting oneembodiment of a process for extrapolating from statistical data gatheredfrom mobile devices at a live event 3300. The process begins at step3302 where, during dead time at the live event, a polling question isdisplayed on a venue display screen. The displayed question may be on alarge variety of topics, such as consumer preferences and politicalquestions. For instance, a question may ask “what type of car do youdrive?”, with options listed for “compact car,” “full-size car,”“truck,” “SUV,” and “other.” The user would select their answer bypressing a button on the screen of the mobile device that corresponds tothe option presented on the venue display screen 108. On the other hand,a question such as “are you in favor of gun control?” with the options“yes,” “no,” and “undecided” presented. It will be appreciated thatstatistical data may also be gathered from activities other than pollingquestions.

Similarly, questions to assess merchantability may also be presented onthe venue display screen. Merchantability for the purposes of thepresent invention includes gauging whether consumers would buy orendorse a particular product. For example, a question may be presentedthat asks “would you buy Pepsi or Coke?” or “would you buy a Chevy?”.The question may be accompanied by other media, such as showing an adfor Pepsi following by an ad for Coca Cola, and then displaying thequestion. In another example, a live concert event may ask the audience,after the first two bands have played and while the headlining act issetting up, which of the first two opening bands the audience prefers,or for which of the two bands the audience would buy a record.Similarly, this may be done by playing music videos of bands unrelatedto those playing at the concert, followed by a question regardingwhether the audience would by an album for the bands, and for which one.In yet another example, a band at a concert may ask the audience tolisten to a few melody selections, which the band would play live forthe audience, and then present the audience with a question regardingwhich of the melodies most resonates with the audience. This wouldprovide useful data for the band or record label in determining whattypes of melodies the general audience is most attracted to, and thenbase future songs on that preference. It will be understood that theseare just a few examples, and the same concepts could be applied to alllive events and within all industries.

At step 3306, all answers from attendees who participated in step 3304is saved, either to the local central office 120 or the central remoteoffice 126. At step 3308, statistical conclusions are drawn byextrapolating from the answers received from the attendees. Theattendees are essentially serving as the sample for the statisticalanalysis. However, unlike most studies or focus groups, where you have asmall sample size and extrapolation is performed based on that smallsample, a live event such as a sports or music event can potentiallyhave tens of thousands of people, or even more. Thus, this provides formore accurate conclusions to be drawn. Further, the data is collectedfrom people who may already be interested in the subject-matter. Forinstance, an audience at a concert that is being asked for theirpreferences in music are exactly the type of people a record label wantsto hear from; the people who pay to attend concerts. Ordinary focusgroups often lack the core or target audience, and thus conclusionsdrawn from such data can lead to erroneous results. The results of thestatistical conclusion may be displayed at the live event forentertainment purposes wherein people can measure their response againstwhat the most popular response was, for example. The results may alsoonly be viewed or analyzed by interested parties.

This process also allows for statistical conclusions to be based on thetype of audience, or on a population generally, depending on the desireduse of the data. For example, data collected from a live baseball gamewhere the question “what type of car do you drive?” might be applied tobaseball fans. So, if a majority of fans selected “truck” as theiranswer, a conclusion could be drawn that baseball fans are more likelyto buy trucks than other types of vehicles. Similarly, if the samequestion is asked at an opera house, and a majority of the audienceselected “full-size car,” a conclusion could be drawn that opera fansare more likely to by full-size cars than other types of cars.

In addition, this process allows for attendees to measure themselvesagainst other attendees in some embodiments. After an attendee hasanswered a question, the results may be compiled and re-displayed forthe audience to view how their response measures against the audience asa whole. It will be appreciated that this has many applications. Forinstance, if a majority of the women in the audience answered a questiona particular way, while a majority of the men in the same audienceanswered a question in a different way, this may be compiled andre-displayed for the audience. This allows for the audience to see howgender affected the results, and can measure their answer against themajority opinions. This creates an interest in answering the question,as there may be a natural curiosity to see how one's answer measures upto the results. The same method could be applied to merchantabilityquestions, such as re-displaying the results of the question “would youbuy Pepsi or Coke?” so that an audience member can see how his or heranswer measures against the results. For instance, if someone chose theresponse option for “Coke,” and the results are re-displayed to showthat 90% of those who answered chose “Pepsi,” the person who chose“Coke” can see that they are in the minority.

This system may also be extended to questions that elicit more emotionalresponses. These types of questions would relate to how people feelabout a particular topic. For instance, the question might state “how doyou feel about the government?” or “how do you feel about theneighborhood you live in?” with appropriate responses ranging frompositive to negative to even fearful answers. The statistics regardingthese answers would then be displayed to the users. This process mayalso be enveloped in a game. For instance, it might ask one section ofthe crowd to press one response key as fast as they can, while anothersection presses a different response key. The results would then bedisplayed, showing how the sections compared.

The displayed results may be in a variety of forms, bar graphs, piecharts, percentage, ratios, or any other visual representation of data.

Referring now to FIG. 34, there is illustrated a flowchart depicting theoverall audience participation concept, which is initiated at a block3402. Program then proceeds to a block 3404 to send the query or theprompt requesting audience participation in the form of a response in aspecific manner. The program then flows to a function block 3406 todisplay the results. These results are real time results.

Referring now to FIG. 35, there is illustrated a flowchart for oneexample, that of the Wave. This is initiated at a block 3502 and thenproceeds to a block 3504 to phrase the prompt or request on the display108 as a request for the Wave. The program then flows to a functionblock 3506 to display a particular section. The program then flows to ablock 3508 in order to record the response and update the UID. Sinceevery response is in the form of the QID for that particular query, justby looking at a particular QID, the QIDs populating such can bedetermined. For this application, only a single “tap” of the key will beaccepted. If, for some reason, an individual taps the key multipletimes, this would actually be recorded in the QID during thatappropriate time window as a valid input. This might be used for somestatistical analysis, but for the purpose of this application, all thatis necessary is to know how many UIDs have been recorded in that QIDrecord. Each of these UIDs can then be mapped to seats, as indicated bya function block 3510. This is facilitated by the fact that thesephysical location is actually provided as a part of the UID. This can beutilized for such things as delivering prizes, for example, but in thissituation, all that is required is to map the seat number onto thedisplay. It may be that UIDs for other sections also responded, andthese may or may not be displayed. The results displayed are illustratedin a function block 3512. The program then flows to block 3515 in orderto select the next section and then back to the input of function block3506. As each section is displayed, the results are then displayed inreal time. Since this is real time, the time window can be very shortperiod. Thus, after the time period has expired for particular section,no more UIDs for that section will be recorded. Thus, the time window isbasically a “wide-open” time window that is only for the purpose ofdisplaying results. When the section number is up there, the time windowis open only for that section and those associated UIDs. Alternatively,all UIDs can be displayed that respond, and all that is necessary is tocontinually display a prompt that moved from section to section and thendisplay the results of the MUs 110 that responded via their associatedUID.

Referring now to FIG. 36, there is illustrated a diagrammatic view ofthe overall display of the venue, illustrating a plurality of sections3602. As the UIDs are collected, the particular section can either becompletely eliminated or the shade associated with that particularsection increases a function of the number of UIDs that respond. Thisallows the actual individuals to view the response and asked toparticipate more. Additionally, by analyzing the number of UIDsassociated with that particular QID, it will provide to the analystinformation regarding which sections have people that are actually moreparticipatory than others, among other information.

Referring now to FIG. 37, there is illustrated a flowchart for the“noise” interactive operation. This is initiated at a block 3702 andproceeds to a block 3704 in order to request multiple taps from females,in one example. The program then flows to a function block 3706 in orderto collect in the QID for that query/prompt all of the UIDs respondingthereto. This particular query might also have an SCID that isassociated with gender. Suppose, for example, that the button that theyrequested to be pushed for this particular operation was the “A” button.At this point in time, that particular button is statistically linked toan SCID that has been predetermined to have a certain statisticalrelationship to the individual pressing a button being a female. Asnoted hereinabove, that is probably not 100%. It may be 90% due to thefact that certain people did not read the prompt correctly or they donot care. In any event, an SCID for 10% male/90% female could beassociated with that particular button.

At this point, the overall density for the responses in total justlooking at the QID can be determined, as indicated by a block 3708. Theprogram then flows to a function block 3710 to map this to the seats inthe venue 102 and then to a block 3712 to display the results. It couldbe that the more “taps” that a particular MU 110 is associated with, thehigher the density for that particular seat will be. An average of theoverall density in a particular region about all the seats can bedetermined or the actual seat itself can be made brighter, for example,due to the density of taps associated there with.

A new query is then generated requesting multiple taps from males, asindicated by block 3714. Again, this is a QID that is unique as opposedto the QID for requesting multiple taps from females. The program thenflows to a decision block 3716 to determine if this is to go back aroundto collect the UIDs for this QID and, if so, the program flows to theblock 3706 to collect UIDs for this particular QID associated with thisparticular query/prompt. This continues until it's finished and then, atthe decision block 3716, it flips back over to the input of functionblock 3704 to request the multiple taps from the females in theassociated query therefore. Each of these queries associated with thatparticular QID has a time window associated therewith, of course, asthis makes a QID. Thus, for this purpose, a second QID could begenerated each time a new query is generated, or the program couldmerely create the same QID with a different time window. However, foranalysts, it will be desirable to have a separate QID for each timewindow such that, for example, if there were 10 requests for females torespond and 10 request for males to respond, there would actually be 20QIDs created in the system. These would all be queued up for output at aparticular time. Overall, this could actually be an automatic operation.

Referring now to FIG. 38, there is illustrated a diagrammatic view of avenue display 3802 displaying various sections. There are illustrated intwo detailed section views 3804 and 3806 two sections. In the sections,there are provided density distributions for the responses. As notedhereinabove, this could actually be a heavy shading. However, withknowledge of the UIDs and their seat location, any type of displayoperation can be provided.

Referring now to FIG. 39, there is illustrated a flowchart for acontest. This is initiated at a block 3902 and then proceeds to a block3904 to generate and send a query requesting a rapid tap with some typeof incentive for doing such. For example, the incentive could be that apromotional T-shirt will be given to the individual or individuals thatcould tap the fastest on their MU 110. Program then proceeds to a block3906 in order to start a time window for the overall promotion and thento a function block 3908 to accumulate the various information in theQID and in the SCID, in addition to accumulating such in the UID. Atthis point, all of the database is updated in the various records. Ifall that was desired was to determine which MU 110 had the most taps, itwould only be necessary to determine how many times the QID for thatparticular query was updated by the UID, which is reflected in thepointer back to the UID, as a UID record has a record of each QID thatit responded to. If a time window were 10 seconds, a countdown timer onthe prompting display might show the countdown, but the time windowwould not necessarily be exactly synchronized to that countdown timer.At the end of time, the number of times that the QID was responded to bya particular UID can be determined and that particular seat numberdeclared a winner. The end of the time window is indicated by a decisionblock 3910, at which time an analysis is made of the data at a block3912 and in the winner determined at a block 3914. At this point, theactual UID can be mapped for the winner to the seat, as indicated byblock 3916 and then some type of announcement of that result indicatedat a block 3918. The result could be that a promotional T-shirt isdelivered to multiple seats surpassing a threshold, delivered to asingle seat, or an announcement made that for a particular seat, thepresentation of your ticket, would allow the individual to collect theirprize at later time. In addition, it could be that a raffle was providedof an automobile, for example. That automobile could be advertised asone that would be given away at the end of the event, and based uponresponses the automobile manufacture would provide this promotion andutilize the number of taps to place individuals into the raffle, or theycould just merely be a response that places them in there. This responsewould be for the purpose of, for example, collecting statistics from theparticular crowd. This would allow a large number of seats, take, forexample, 8000 responders out of the 10,000 attendee event to be placedinto the raffle. This would be an incentive for people to pay attentionto the screen and, further, it would actually encourage people toactually activate their applications and enter crowd based responsesystem.

In the event that MU 110 is not entered into the crowd based responsesystem due to the fact that they did not register, it is possible toallow them to register. This would be a prompt that would actually askpeople to turn on their application and respond to a particular responserequest in a certain manner, as entering whether they are male orfemale, i.e., pressing button “D” or “B.” the preferable way, of course,is to register when they enter the stadium. The reason for this is that,at this time, registration would require another payment, and thispayment may be entered at the middle of the overall event. This might betreated different by the overall financial arrangement with the eventplanners.

Referring now to the FIG. 40, there is illustrated a diagrammatic viewof the overall “tapping” response. The MU 110 is illustrated as havingone button that is the focus of the particular query or promotionaloffer, this requesting the individual 202 to select this particularbutton, in this case, the “C” button. This will result in the particularUID for that particular MU 110 to collect both QID occurrences and SCIDoccurrences. Again, this particular request could be for females to tap,and the SCID for that would be different than a request requesting allmales to respond. Any one of these can be summed, as indicated by ablock 4002, in order to provide a total, in one example. As notedhereinabove, any type of analysis can be made upon this particular UID.Here, the issue is to determine how many times a particular UID has beenassociated with a QID within the time window of the QID.

Referring now to FIG. 41, there is illustrated a diagrammatic view ofsets of media messages that can be presented to event attendees. Eachset of media messages has several different versions of a media messageassociated with it. Media messages are information presented to theevent attendees in the form of audio, video, or still image messages. Amedia message can be either recorded or live. As an example, a firstmedia message set 4110, referred to for ease of readability as M1, mightbe a set of advertisements for vehicles. This set might be comprised ofmessage M1A, an advertisement for a motorcycle, message M1B, anadvertisement for a truck, and message M1C, an advertisement for asports car. A second media message set 4120, which will be referred toas M2, might be a set of descriptions of prizes that can be won in fancontest at the event. This set could be comprised of video message M2A,a description of a an autographed T-shirt from a sports team captain,video message M2B, a description of a piece of sporting equipment usedby a professional athlete, and video message M2C, a description of apair of free tickets to a future event at the event venue. A third mediamessage set 4130, referred to as M3, could be a set of short videosegments from various upcoming movies or television shows. A fourthmedia message set 4140 (M4) is provided to illustrate that the number ofmedia messages in a set of media messages will be different in otherembodiments. Some embodiments contemplated will have media message setswith only one message, while other embodiments will have media messagesets with up to hundreds of messages. Although specific embodiments havebeen provided as examples, a media message can take the form of anymessage, including advertisements, announcements, news updates,entertaining interludes between parts of the event, or even politicalmessages. In some embodiments, sets of messages can have multiple typesand formats of messages. For example, in one embodiment a message setwill include a video advertisement message and an audio news updatemessage. In some embodiments, for each set of messages, which specificmessage is presented is based on the statistical make-up or preferencesof the event attendees or even just the participating users. One way ofselecting the specific media message to present is by analyzing thestatistical data of responses to queries, as is described herein below.

Referring to FIG. 42, there is illustrated a diagrammatic view ofqueries, each with multiple associated query response statisticalconditions and messages corresponding to those response statisticalconditions. The specific message that is presented after a query isdetermined by which of the query response conditions is met. Forexample, query 4210, referred to as Q1, has associated responsestatistical conditions C1A, C1B, C1C, and C1D, each corresponding to amedia message, M1J, M1K, M1L, and M1M, respectively, which is presentedto the attendees if the corresponding condition is met. Similarly, query4220, referred to as Q2, has associated response statistical conditionsC2A, C2B, C2C, and C2D, each corresponding to a media message, M2J, M2K,M2J, and M2M, respectively, which is presented if the correspondingcondition is met. Note that for query Q2, two of the conditions, C2A andC2C, have the same corresponding media message, M2A. This is toillustrate that in some embodiments, each condition need not have aunique message, and that multiple conditions have the same correspondingmedia message. Query 4230, referred to as Q3, is presented todemonstrate that the number of conditions and corresponding mediamessages associated with a query varies in other embodiments. Althoughthe examples presented have four conditions with corresponding mediamessages, other contemplated embodiments have queries with only onecondition and media message, while other embodiments have as many ashundreds of conditions and media messages. It should also be noted thatin some embodiments, some conditions will result in multiple messagesbeing presented. In some embodiments, multiple response conditions beingmet will result in multiple media messages being presented.

The process of analyzing the statistical data of responses to a query toselect a media message is described herein. UID information and queryresponses can be used to influence subsequent media messages presentedto the attendees of the live event. Referring to FIG. 43, there isillustrated a flowchart depicting the operation of selecting a mediamessage based on the input of event attendees in response to a query.The process starts with start block 4302 and proceeds to decision block4304 which decides if a media message will be presented before thequery. If no pre-query message is to be presented, the process moves tofunction block 3008, where a query is presented to the event attendees.If a media message is to be presented before the query, the processproceeds to function block 4306, where the media message is presented,then to function block 4308, where a query is presented to theattendees. After the query is presented, the process goes to functionblock 4310, which receives responses from the MUs 110. Next, a decisionblock 4312 determines if the time window for responses is still open. Ifso, the process loops back to block 4310 to continue receivingadditional responses. If the window is closed, the operation proceeds toblock 4314, where the responses are analyzed associated with UID data.Next, function block 4316 applies the statistical response data andpreviously known statistical UID information to selection criteria andchooses the next media message to be presented. The program thenproceeds to function block 4318 where the selected media message ispresented to the event attendees. The program moves to block 4320 andends.

FIGS. 44 and 45 depict an example of how responses from MUs 110 toqueries can be used to influence subsequent media messages. Referring toFIG. 44, there is illustrated an image representing an example of videomedia message to be presented to attendees of a live event. In otherembodiments, the media message is a public announcement, anadvertisement, a news update, a political message, or an entertaininginterlude. The media message can be either audio, visual, or video. Inthis example, however, the image represents the first part of a videoadvertisement for a pickup truck. This first part of the advertisementshows a truck 4420 driving down a road 4422 until it arrives to andstops at a point 4424 where the road splits into three different paths.Each of the paths leads to a different type of driving destination. Path4426, Path “A,” leads to driving destination 4440 which includes a trailin the mountains. Path 4428, Path “B,” leads to driving destination 4442which includes a trail in the forest. Path 4430, Path “C,” leads todriving destination 4446 which includes a trail in a valley with a riverand a lake. At this point, the first part of the advertisement concludeswith a message that informs the attendees that they can help decidewhere the truck will go next by choosing which path the truck will take.A query is then presented which prompts the attendees to use their MUs110 to make a selection indicating their preferred path for the truck totake. For instance, the query might say, “Which adventure do YOU want totackle during halftime?” The query would prompt the event attendees toselect “A” on their mobile devices if they want to see the truck go downPath A and drive through the mountains, “B” if they want the truck to godown Path B and drive through the forest, or “C” if they want to see thetruck take Path C drive through the valley with the river and lake.

Referring to FIG. 45 and continuing with the example of the truckadvertisement, the second part of the advertisement video is presentedafter the time window for response queries has ended. The second part ofthe advertisement will be one of three video messages. The first videomessage, demonstrated herein with example image 4510, shows the truckdriving through the mountains. The second video message, demonstratedherein with example image 4520, shows the truck driving through theforest. The third video message, demonstrated herein with example image4530, shows the truck driving through the valley with the river. Whichvideo message is played will depend on statistical data from the queryresponses. For example, if, in responding to where they would like tosee the truck go next, more UIDs choose “A,” indicating the user wouldlike to see the truck drive through the mountain path, than “B” or “C,”then the video message showing the truck driving through the mountainswould be presented as the second part of the truck advertisement. Inthis manner, the event attendees “have a say” in what messages arepresented to them.

The example depicted in FIGS. 44 and 45 represent an embodiment of thedisclosure. As previously noted, other embodiments will have mediamessages that are video, audio, or visual in nature, or evencombinations of the three, and will include live and pre-recordedmessages. Some embodiments will have media messages presented before thequery, while other embodiments will not. The truck advertisementdescribed is only one type of content. The media messages in otherembodiments include public announcements, news updates, instant replays,video or audio feed from events at other locations, advertisements,event announcements, or any other notification or message useful toevent attendees. Also, the response conditions leading to each mediamessage will not be as readily apparent to the event attendees in someembodiments as in the presented example of the truck advertisement. Insome embodiments, the event attendees will not know how their queryresponses will affect the subsequent media message or messages. Someembodiments will not present a query as simple as “which message wouldyou like to see?” but will nonetheless base post-query media messages onstatistical information from the query responses. In some embodiments,the mobile users will not even know that their responses to queriesaffect the choice of the subsequent media message or messages.

The selection of which message to present after query responses havebeen received and analyzed can be based on various kinds of statisticaldata. The media message selection could be based solely on which choicereceived the most “votes” from the users, or the message selection couldbe based other statistical information gathered from the responses.Since demographic information can be associated with UIDs, an advertiseror media partner might choose to present a message based on theresponses from UIDs associated with a certain demographic, such as acertain gender or household income. Continuing with the example of thepickup truck advertisement, the manufacturer of the truck in theadvertisement might be especially interested in a particular segment ofthe event attendee population, such as males, and might focus on theresponses from the UIDs associated with male users. For example, if 60%of responding UIDs indicated “C,” the valley with the river, as thepreferred destination for the truck, but 70% of responding UIDsstatistically associated with males indicated “A,” the mountain trail,as the preferred destination, the video represented by image 4510showing the truck driving through the mountains might be presented asthe second part of the advertisement if the truck manufacturer isparticularly interested in keeping the attention of male eventattendees.

In another embodiment of the disclosure, the query presented to thevenue attendees prompts users to input a response into their mobileunits as a way to determine which portions of the venue have the mostattendees paying attention to the queries and multimedia messages.Referring now to FIG. 46, there is illustrated a sports stadium 4610,with venue attendees 4612. (In other embodiments, the venue will be aconcert hall. In yet other embodiments, the venue will be a theater.)The seating of stadium 4610 is divided into multiple seating sections,labeled “Section A,” Section B,” and “Section C.” Stadium 4610 includesa large display 4620 which can be seen by the event attendees. Thedisplay presents a message to the event attendees prompting them torespond with some kind of input into their MUs 110, such as by tappingrapidly a “button” on an application running on their MUs 110. Themessage prompt will be displayed for a predetermined time window, forexample, fifteen seconds. The system records the number of tap responsesfor each UID. Since each UID can be associated with a seat location inthe event venue, the system can determine which seating section had themost tap responses during the time window. Which seating section had themost taps will affect the subsequent media message that is presented tothe event attendees. In some embodiments, the section with the most tapswill see a message indicating each attendee in that section wins aprize. In other embodiments, the message might simply indicate whichsection produced the most taps. In some embodiments, seating sections insporting events are divided based on which team the attendees support.For example, one seating section could be comprised mostly of “hometeam” fans, while another seating section is comprised mostly of “awayteam” fans. In some of these embodiments, the post-query message will bea message related to the sports team favored by the seating section withthe most taps.

In other embodiments, the taps are not sorted by seating section, butare sorted by some other type of information, such as demographicinformation associated with the UIDs. For example, some embodiments thetaps are categorized by male or female UIDs. In other embodiments, theyare categorized by age range. In other embodiments, they are categorizedby the team or player the attendees' support, regardless of where theattendees' seats are.

In a variation of the embodiment illustrated in FIG. 46, event attendeestap their MUs 110 in response to a query that takes the form of a game.Event attendees (and thus their UIDs) are divided into groups as teams(for the purposes of the tapping game). The number of teams can beanywhere from only a single team to several dozen teams. A visual oraudio query message then prompts attendees to “tap” their MUs 110 asmany times as they can during a predetermined time window. Once the timewindow has expired, a media message communicates to the attendees howmany total taps were performed by the UIDs associated with each team. Insome embodiments, the process will be repeated, with multiple promptsand time windows for tapping. At the end of each time window, a mediamessage will communicate the number of taps per team for the latest timewindow, as well as the cumulative number of taps for each team duringall of the time windows during the event. The game can be presented inseveral ways. In some embodiments, it will take the form of a virtualrace taking place on a video screen over several time windows. Each teamhas an associated horse, person, dog, colored dot, or any other objector animal typically involved in a race. The more taps each teamaccumulates during each time window, the farther down the “track” thatteam's racer will travel at the end of the time window. Each race couldlast only one time window, or a race could span several time windows,with each time window representing a “leg” of the race and beingaccounted for in the “leg” during the next time window.

Referring to FIG. 47, there is illustrated an example embodiment whichdisplays a message based on the gender make-up of the event attendeesthat respond to a query to “check-in” with their MUs 110. Query 4710 isa message that is presented to the event attendees, in either an audioor visual format. Query 4710 prompts event attendees 202 to “check-in”using the application on their MUs 110. This query is presented any timethat the event proprietors wish to know the demographic make-up of theattendees paying attention to the queries and multimedia messages. Insome embodiments, the query will prompt to attendees to “check in” to beentered into a contest. The system will allow attendees to “check in”during a predetermined time window following the presentation of query4710. Once the time window has expired, the system will determine, basedon information associated with the UIDs that “checked-in,” thedemographic make-up of the respondents. The message presented after thetime window has expired depends on the analysis of the demographicmake-up of the attendees that responded. In the example embodimentpresented, the system determines the gender make-up of the attendees“checking-in.” The post-query message presented is at least partiallydetermined by this gender make-up. Situation 4720 illustrates that if asignificant majority of the attendees checking in are female, afemale-oriented message is presented. Situation 4730 illustrates that ifthe numbers of males and females checking in is roughly equal, or notsignificantly weighted one way or the other, a gender neutral message ispresented. Similarly, situation 4740 illustrates that if the number ofmales checking is significantly greater than the number of females“checking-in,” a male-oriented message is presented.

In different embodiments, different demographics will be analyzed. Forexample, in some embodiments, the check-ins will be categorized by theage of the user associated with a particular MU 110. In otherembodiments, the check-ins will be categorized by geographic locationsof the attendees' homes. In other embodiments, the check-ins will becategorized based on sports team affiliations.

Referring now to FIG. 48, there is illustrated a diagram of the way inwhich an embodiment of the system uses queries and responses todetermine which media message to present next. Illustrated in thediagram are several media messages 4810. In some embodiments, thesemessages are in video form, while in other embodiments, the messages arein audio form. Also illustrated are several queries 4820, which are alsoeither audio or video in form. Finally, sets 4830 of responses toqueries 4820 are illustrated. These sets 4830 of responses are responsesfrom event attendees and are inputs into MUs 110. The system starts bypresenting media message M1. After media message M1 is presented, queryQ1 is presented to the event attendees, prompting the attendees to inputa response into their MUs 110. After receiving the responses that makeup the set R1 of responses, the system analyzes R1. Next, a second mediamessage 4810 is presented. The media message will be M2, M2′, or M2″.Which media message is presented is based at least in part on theanalysis of R1. After the second media message is presented, the systempresents third media message, or if M2″ was the second media messagepresented, presents query Q2 to the event attendees, prompting responsesthat will form set R2. The third media message will be M3 (if M2 waspreviously presented), M3′ (if M2′ was previously presented), or eitherM3A″ or M3B″ (if M2″ was previously presented). If M2″ had been thesecond message presented, then the analysis of R2, the set of responsesto query Q2, will be a factor in determining which one of M3A″ and M3B″is presented. Finally, a last media message will be presented. If M3′was previously presented, M4′ will be presented. If M3A″ was presented,M4A″ will be presented, and if M3B″ was previously presented, M4B″ willbe presented. If, however, M3 was the previously presented message, theneither M4A or M4B will be the next message. After M3 is presented, queryQ3 will be presented to the event attendees, prompting responses thatwill form set R3 of responses. The analysis of R3 by the system willfactor into which one of M4A or M4B will be presented to the audience.

The example embodiment shown in FIG. 48 illustrate the aspect of thedisclosure whereby the messages being presented to event attendees can“branch” onto different possible paths based on user responses toqueries. This branching can allow for a “story” to be told through themessages, with the direction of the “story” being at least partiallycontrolled by the event attendees (or the statistics associatedtherewith) as they respond to the various queries. In some embodiments,the “story” is an entertaining narrative, while in other embodiments, itis a series of related advertisements. In some embodiments, the eventattendees are informed that their query responses affect which messageswill be presented next, while in other embodiments, the event attendeesare not informed of this. The specific number of media messagespresented, as well as the number of queries will vary in differentembodiments. Some embodiments have a query and a response analysis afterevery message. Other embodiments only have queries presented aftercertain messages. In some embodiments, the selection of a post-querymessage will be based solely on the statistical information from theresponses. In other embodiments, the post-query message will also bebased on other previously gathered statistical data about the UIDs.

Referring to FIG. 49, there is illustrated an embodiment whereby theevent attendees influence the choice of a music video to be presented.In this embodiment, a media message represented by block 4910 in theform of short video clips is presented to the audience. The messagecomprises of short clips of music videos, each clip being of a songperformed by a different band or group. Next, a query 4920 is presentedto the event attendees prompting them to select which band or group theythought was the best or the most entertaining. At block 4930, the eventattendees user their MUs 110 to select their choice for which band orgroup performed the best. Next, at block 4940, the system compiles andanalyzes the responses of the attendees and generates statisticalinformation about the responses. In some embodiments, this informationwill include previously obtained demographic data about the respondingUIDs. Following analysis of the responses, a media message in the formof the full-length version 4950 of one of the music video clips will bepresented to the attendees. Which full-length version 4950 will bepresented is at least partially influenced by the statistics of theresponses to the query 4920. In some embodiments, the video clip thatreceived the most “votes” from responding attendees will have itsfull-length version presented as the post-query media message. In otherembodiments, the choice of which full-length video to present will bedetermined by which clip receives the most “votes” from a specificdemographic, such as a particular gender, age group, nationality, orincome level.

In some embodiments, responses to queries will affect the choice ofsubsequent queries to present to event attendees. Turning to FIG. 50,there is illustrated a diagram showing how the available queries to bepresented are organized into selection “trees.” Query 5002 (Q1)represents the first query in the query tree to be presented to theevent attendees. After attendees submit responses making up response set5004 (R1), the system analyzed the responses to R1 using an algorithmtailored for analyzing responses to Q1. The results 5006 of thisalgorithm are a function of R1 (represented by F(R1)). The next querypresented to the event attendees will be one of the queries 5008, eitherQ2A or Q3A. The selection of which of these queries will be presented,and which “branch” of the query “tree” to travel, is at least in partdetermined by F(R1). The process continues again, where either Q2A orQ2B is presented to the audience. Q2A or Q2B will then be followed byone of the responses 5010 R2A or R2B, respectively. The response setthat is obtained will then be analyzed by another function, with theresults of this function represented by blocks 5012. The processcontinues, branching into different paths to one of queries 5014 as thequeries and responses help determine which subsequent queries topresent. In some embodiments, there will be multiple queries at every“level” of the tree. In some embodiments, not every query will have theability to branch off to multiple subsequent query paths. In someembodiments, one or more of the queries will have more than two possiblepaths to branch to, while in other embodiments, queries with multiplepaths will only have two possible paths.

Turning to FIG. 51, there is illustrated a flowchart showing the processof using the analysis of query responses to select the next query to bepresented. The process starts with Start block 5102 and flows tofunction block 5104, where a query is presented to the event attendees,prompting a response. The process moves to block 5106, where theattendee responses are received by the system, then to block 5108, wherethe responses are processed and stored by the system. Next, at block5110, the responses are analyzed for statistical information. Next, atdecision block 5112, if there are no more queries to be presented, theprocess ends at End block 5114. If, however, more queries are to bepresented, the process moves to block 5116, where the responses to theprevious query are analyzed with an algorithm tailored to that query.Next, the process flows to block 5118, where the results of the analysisfrom block 5116 are used to select the next query to be presented.Finally, the process closes the loop to 5104 and presents the newlyselected query.

Referring now to FIG. 52, there is illustrated a flowchart depictinganother embodiment for the operation of the merchandising feature areamethod 5200. The method 5200 begins at step S202 where a vendor makesitems available for purchase, the items being associated with visualindicia. For instance, one item might be associated with the letter “A,”one item might be associated with the number “5,” one item might beassociated with a triangle symbol, and so on. It will be understood thatthe vendor could be any type of vendor that sells goods, such as retailstores, direct sales salesmen, vendors that sell items on the internet,vendors that sell items on television, or any other type of vendor. Atstep S204, a customer views the available items. At step S206, thecustomer chooses one of the items to purchase by selecting on thecustomer's mobile unit the specific visual indicia among the displayedgroup of visual indicia associated with the item the customer wishes topurchase.

At this point, a button ID with a timestamp is created. It is providedin order to encode within the button ID the information about the buttonunique to the application, i.e., a unique button code. This is thencombined with a timestamp and sent via the overriding offer application,which is associated with the screen 2308, to the provider 2306 in orderto interface with the vendor 2312 or to just send the information tovendor 2312. This is indicated at a block 5208. The program then flowsto block 5210, wherein this is sent via the MU 110 to the carrier andthen to the vendor 2312. At step S212, the overall order is processed.This process includes verifying payment and then sending order/paymentto the vendor, as indicated by block 5214. The vendor will then deliverthe product as indicated by block 5216, and the customer is billed bythe provider, as indicated by block 5218.

Referring now to FIG. 53, there is illustrated a diagrammatic view ofone embodiment of a system for providing items to be sold based onvisual indicia within the range of a beacon. There is shown a sales area5302. This may be the confines of a retail location, the confines of ahome where direct sales are taking place, or any other location wheregoods may be sold. Within the sales area 5302, there are a plurality ofbeacon areas 5304. The plurality of beacon areas 5304 may be achievedusing various forms of wireless networks or other configurations. Forexample, the plurality of beacon areas 5304 may be WIFI networks,Bluetooth networks, BLE networks, Zigbee networks, Thread networks,wireless mesh networks, achieved with geo-fencing, or any other methodof creating a beacon area that a customer may use to connect to all ofthese non-overlapping networks. Within the plurality of beacon areas5304 may be a plurality of items for sale 5306. Each item of theplurality of items for sale 5306 is marked with a particular visualindicia that is unique from the visual indicia used to mark the otheritems within the plurality of items 5306. For example, if a t-shirt ismarked with the visual indicia of “A” within one beacon area, therewould be no other item within that same beacon area marked with thevisual indicia of “A.” However, another item within a different beaconarea may be marked with the same visual indicia. For example, if at-shirt is marked with the visual indicia of “A” within one beacon area,a pair of shoes that is within another beacon area may also be markedwith the visual indicia of “A.” Thus, the beacon area representsproximity to the goods.

Referring now to FIG. 54, there is illustrated a flowchart depicting oneembodiment of a method 5400 for purchasing items to be sold based onvisual indicia within the range of a beacon. The method 5400 begins atstep S402 where a vendor makes items available for purchase, with theitems being within a range of a wireless beacon and the items beingassociated with visual indicia. For instance, one item might beassociated with the letter “A,” one item might be associated with thenumber “5,” one item might be associated with a triangle symbol, and soon. It will be understood that the vendor could be any type of vendorthat sells goods, such as retail stores, direct sales salesmen, vendorsthat sell items on the internet, vendors that sell items on television,or any other type of vendor. At step S404, a customer enters the rangeof a wireless beacon and connects to the wireless beacon in the mannerappropriate for the particular beacon type or just receives a signalfrom the beacon. This connection may be achieved automatically, or thecustomer may have to manually connect to the beacon. At step S406, acustomer views the available items. At step S408, the customer choosesone of the items to purchase by selecting on the customer's mobile unitthe visual indicia associated with the item the customer wishes topurchase. At decision block 5410, the system determines if the customeris within range of multiple beacons. This may occur if there areoverlapping beacon areas. If so, the process moves to step S412 wherethe user is prompted to verify his beacon ID. The customer may knowwhich beacon he is connected to based on the name of the organization heis shopping with, or if the organization has multiple beacon areas, theparticular physical area within the beacon area may have signage that ismarked with the beacon ID so that the customer knows which beacon areahe currently is within. The process then moves to step S414. If atdecision block 5410 it is established that the customer is not withinrange of multiple beacon areas, the process also moves to step S414.

At this point, a button ID with a timestamp is created. It is providedin order to encode within the button ID the information about the buttonunique to the application. This is then combined with a timestamp andsent via the overriding offer application to a service provider/mobilecarrier in order to interface with the vendor selling the items to besold or to just send the information to vendor. This is indicated atstep S414. The program then flows to block 5416, wherein this is sentvia the MU 110 to the carrier and then to the vendor. At step S418, theoverall order is processed. This process includes verifying payment andthen sending order/payment to the vendor, as indicated by block 5420.The vendor will then deliver the product as indicated by block 5422, andthe customer is billed by the provider, as indicated by block 5424. Theproduct may be shipped to the customer, or the vendor may simply handthe item to the customer if the customer is at a vendor location, tosatisfy step S422.

Overall, the use of a beacon for determining proximity to a particularlocation can be achieved by disposing a very small device such as BLEdevice or a Zigbee device proximate to the appropriate goods. This canbe a very low powered beacon that just emits, in one embodiment, andidentifying a signal which is basically a wireless broadcast containingthe unique ID of the particular beacon. This beacon could be morepowerful which would allow it to transmit not only its unique ID butalso information associated with that unique ID. Thus, when a user witha portable device running proprietary application comes into proximityto the beacon, i.e., the transmit range of beacon, it will have anappropriate radio associated with that beacon contained internalthereto. This radio will constantly scan the area for signals associatedwith a particular type of beacon. However, other types of devices mightuse the same technology and transmit their IDs. Thus, when a scan iscomplete and, for example, multiple unique IDs are retrieved, it may bepossible that additional information associated with the ID can allowone to distinguish this particular beacon as being associated with theoperation of the application. Alternatively, a lookup table can beprovided for determining if a particular unique ID is associated withthe application.

When a match is found, this indicates that the beacon is associated withthe goods. All that is required is for the mobile unit to transmitinformation regarding the unique ID associated with the button pressedand the beacon ID (or information associated with the beacon) back to acentral office for processing. As described hereinabove, that processingcan be performed at the mobile carrier or at a separate site. It isimportant, however, that the mobile carrier is accessed because themobile carrier contains the billing information and the mobile carrieris the vehicle by which the billing is carried out. Thus, theinformation could be sent to the mobile carrier which is then relayed tothe store associated with that unique ID and then the vendor confirmsthe existence of the product that is ready for shipping and coordinateswith the carrier to confirm that the carrier will bill the customer forthe goods, at which time the confirmation by the carrier of the billingwill result in the vendor being authorized to send the goods to thecustomer via a mailing address associated with customer account numberor, alternatively, the vendor could be authorized to actually transferpossession of the goods at the location.

An alternate operation, the entire operation could be triggered by themobile unit coming into proximity with the beacon. When the beacon isstanding and a recognition of the unique ID indicates that it isassociated with the application, the full application is launched andthe above process carried out.

In some embodiments, the mobile units 110 are part of and transmit theirresponses through one or more mesh networks. Referring now to FIG. 55,there is illustrated an overhead view of venue in the form of a stadium5511. In some embodiments, the venue is instead a theater, while inothers the venue is a concert hall. Inside the stadium 5511 are aplurality of mesh networks 5521. The mobile units within the stadiumconnect to and join one of the mesh networks 5521. Since the coveragearea of each of the mesh networks is limited, several mesh networks arepresent in the stadium to ensure that no matter where a mobile unit isin the stadium, there is a high likelihood that a mesh network will beavailable and in range so that the mobile unit can connect to it. Eachmesh network is connected to external networks via hotspots 5531. Thehotspots 5531 facilitate communication between each mesh network 5521and whichever external networks the hotspots are connected to, includingthe LCO 120 and any intermediary networks between the LCO and thehotspots.

Referring now to FIG. 56, there is illustrated a plurality of meshnetworks 5611 within the stadium 5511 illustrated in FIG. 55. Tomaximize the chance that any given mobile unit will be in range of andwill be able to connect to a mesh network, the mesh networks will havecoverage areas that overlap with each other as shown FIG. 56. Ideally,any time a mobile unit exits the coverage area of one mesh network, itwill already have entered the coverage area of another mesh network.That way, when a mobile unit moves outside the range of the mesh networkit is a part of and leaves that network, it will simply join a meshnetwork with a coverage area that overlapped the mesh network it left.This will maximize the ability of mobile units to stay connected to meshnetworks and minimize the areas of the stadium 5511 where mobile unitswill not be able to connect to any mesh network.

Referring now to FIG. 57, there is illustrated a more detailed diagramof several mesh networks and a hotspot as would exist in the stadium5511 in FIG. 55. Each mesh network 5711 is comprised of multiple mobileunits 110 and a bridge device 5721. The mobile units 110 are connectedto the bridge device 5721 either directly or via other devices in themesh network. The function of the bridge devices 5721 is to connect eachmesh network, and the devices on each mesh network, to externalnetworks. In some embodiments, the bridge device is connected toexternal networks wirelessly, while in some embodiments, the bridgedevice is connected via Ethernet or another wired connection. In theembodiment illustrated in FIG. 57, all of the bridge devices areconnected to at least one associated hotspot 5731, there being aplurality of hotspots 5731 disposed throughout the venue. The hotspot5731 is, in turn, connected to LCO 120 and any intermediary networksbetween LCO 120 and the hotspot 5731.

As a user walks around the venue, the may leave the receive range of thebridge device 5731 it is associated with. This will require entering anew mesh network when it is within range of a bridge device 5731 forthat network. Since only about 256 devices can be on a mesh network, thedevice will have to find a network with a free spot with its locale.

Referring now to FIG. 58, there is illustrated a diagram of anembodiment of the disclosure which uses a mesh network 5800—for example,Thread or Zigbee—to connect the mobile units to bridge devices whichreceives query responses from the mobile units. These bridge devicesthen forward the mobile unit responses out of the mesh network toexternal networks. One or more bridge device 5810 is directly connected(wirelessly) to a certain mobile unit or mobile units near the bridgedevice 5810. The bridge device 5810 is also connected to an externalnetwork and acts as a bridge between the mesh network and the externalnetwork. In some embodiments, the bridge device or devices are connecteddirectly to local central office (LCO) 120. In other embodiments,however, there is another device which relays information from thebridge device 5810 to the LCO 120. The mobile units connected directlyto the bridge device are represented as mobile units 5820. Double linesrepresent direct connections 5850 between mobile units 5820 and bridgedevice 5810. The mobile units 5820 maintain wireless connections 5850with the bridge device(s) 5810. Mobile units 5820 transmit informationto bridge device 5810, but also receive information from other mobileunits in the network and relay that information to bridge device 5810.Some mobile units are not connected to bridge device 5810, but areinstead connected to other mobile units, including mobile units 5820that are themselves connected to bridge device 5810. These are mobileunits 5830. Mobile-units-to-mobile-unit network connections 5860 arerepresented by single lines connecting mobile units. Mobile units 5830not only transmit information to receive information from other mobileunits and transmit information to other mobile units. Finally, somemobile units are only connected to other mobile units in the network andonly transmit information to other mobile units. These are mobile units5840. The mesh network 5800 is comprised of the mobile units and thebridge device(s) 5810. The mesh network 5800 allows multiple mobileunits to create a network among themselves which includes the bridgedevice 5810. Not all mobile units need to be connected directly to ortransmitting information directly to the bridge device 5810. Instead,the mobile units 5820, 5830, 5840 form a “mesh” of connections whichlink all of the mobile units together within a single mesh network thatis defined by a finite limited number of network members. As long aseach mobile unit is within communication range with at least one othermobile unit in its network, or anything else on the mesh network, eachmobile unit can transmit information to another mobile unit or device onthe network, which will then transmit the information to another mobileunit or device on the network. The information is transmitted from unitto unit until the information is received by a mobile unit or device onthe network that is directly connected to the bridge device. That mobileunit then transmits the information to the bridge device 5510, whichthen transmits the information out of the mesh network to an externalnetwork.

Also, mobile units can be connected to multiple other mobile unitswithin the mesh network. Consequently, if for some reason a mobile unitleaves the network (the mobile unit is turned off, turns off theapplication, etc.) any mobile units connected to it can still maintaincommunication with bridge device 5810 via the other mobile units withinthe network to which they are connected. Mobile units can enter or leavethe mesh network at any time. Mobile units can also change what functionthey serve within the mobile network. For example, a mobile unit mightstart out connected directly to bridge device 5810 as a mobile unit5820. If, however, the mobile unit moves far enough away from the bridgedevice 5810, or for any other reason the connection between the mobileunit and the bridge device 5810 degrades, the mobile unit mightdisconnect from the bridge device 5810 and become a mobile unit 5830within the mesh network. Similarly, a mobile unit 5840 might pick up newconnections to mobile units entering the mesh network, making the formermobile unit 5840 (which only transmitted information) a mobile unit 5830(which receives information from some mobile units and transmitsinformation to other unit). The ability of the mobile units to changeroles within the network at any time adds to the network's robustness.

Turning to FIG. 59, there is illustrated another embodiment using a meshnetwork. In this embodiment, like that shown in FIG. 59, the meshnetwork 5900 includes at least one bridge device 5910 (in someembodiments, connected directly to LCO 120, in others connected to LCO120 via another device), mobile units 5920 connected directly to bridgedevice 5910 (via connections 5950), mobile units 5930 which receive andtransmit information to and from other mobile units within the network(via connections 5960), and mobile units 5940 which only transmitinformation to other mobile units. This embodiment, however, alsoincludes wireless relay stations 5970. The wireless relay stations 5970are part of the mesh network 5970 and fill in any “gaps” in the networkthat might be caused by the absence of a mobile unit in the area. Arelay station 5970 is a member of the network 5970 and can be connectedto the bridge device 5910, to nearby mobile units 5920, 5930, 5940, toother relay stations 5970, or to any combination of the three. The relaystations 5970 do not act as mobile units by providing responses toqueries, rather, they simply relay information from mobile units throughthe network until the information reaches the bridge device 5910. Inthis way, the mesh network 5900 will not be adversely affected by a lackof mobile units in a particular physical location within the eventvenue. Even if a particular mobile unit is not within communicationrange of either the bridge device 5910 or another mobile unit in thenetwork, the mobile unit can still communicate with the network if it iswithin communication range of a relay station 5970 that is part of thenetwork.

The devices on each embodiment of a mesh network presented communicatevia radio frequency using the IEEE 802.15.4 communication standard. Themesh networks use internet protocol version 6 (IPv6) for routingdevice-to-device communication. Therefore, the mobile units and wirelessrelay stations each have unique IPv6 addresses, enabling the routerswithin the mesh network to route information between the devices. Themesh network is comprised of devices acting in four different roles:routers, router-eligible end devices (REEDs), border routers, and enddevices. Routers provide routing services to network devices. Routersboth receive and transmit information to and from other devices on themesh network. They also allow (or deny) new devices to join the meshnetwork. End devices communicate with the network through networkrouters, but do not forward messages from other devices. Router-eligibleend devices (REEDs) are devices which have the ability to act as networkrouters if needed, but generally act as end devices. Finally, borderrouters are provide all of the functionality of normal routers, but theyalso provide connectivity from the 802.15.4 mesh network to anothernetwork, such as a Wi-Fi network or an Ethernet network. Each meshnetwork disclosed has devices serving as border routers and either enddevices or REEDs. Some mesh network embodiments will also comprisedevises filing the roles of normal routers, end devices, or both.

In the embodiment shown in FIG. 58, mobile units 5820, 5830, 5840 allact as REEDs. This is because they can perform routing services andforward messages within the mesh network if needed, but typically willonly transmit messages to other routing devices. In some embodiments,bridge device 5810 acts as a border router, as it performs routingservices within the network, but also routs messages, usually in theform of query responses, from the mesh network to the local centraloffice 120, either directly or via external intermediary networks. Thewireless relay stations 5970 shown in FIG. 59 act as routers within themesh network, since they do not generate their own response messages,but function only to forward response messages from mobile units in thenetwork through the network on to the wireless receiving station 5910.

For a mobile unit to participate in the mesh network, it must first jointhe mesh network by proceeding through either two or three phases:discovery (not always required), commissioning, and attaching. Duringthe first phase, discovery, the mobile unit (the joining device)discovers the mesh network and establishes contact with a device withinthe mesh network acting as a router (or border router or REED). To dothis, the mobile unit scans for network channels and sends out a beaconrequest on each channel. The mesh network, when it is accepting newdevices into the network, will respond with a beacon message comprisingthe network Service Set Identifier (SSID) and a permit joining beacon.The mobile unit, upon receiving the permit joining beacon, has nowdiscovered the mesh network. The mobile unit then uses Message LinkEstablishment (MLE) messages to detect, establish communication, andconfigure a secure radio link with a nearby router with which it canperform the next step, commissioning. The mesh networks in theembodiments presented herein are secure networks. Because of this, newdevices cannot join the network until they are granted access by themesh network in the phase called “commissioning.” It should be notedthat if a mobile unit already has commissioning information, it does nothave to go through the discovery phase. Commissioning can beaccomplished on the mesh network via two different methods. With thefirst method, the out-of-band method, commissioning information isconfigured on the mobile unit directly. This allows the mobile unit tojoin the mesh network as soon as it is introduced to the network. Withthe second method, a commissioning session set up between the mobileunit and an application on another device securely deliverscommissioning information to the mobile unit, at which point the mobileunit is allowed to join the mesh network. In some embodiments, themobile unit will be required deliver a passcode to the mesh network (oran external device controlling which devices may join the network)before commissioning information will be furnished to the mobile unit.In some embodiments, this passcode will be furnished to the eventattendees by the event venue. The final phase of joining the meshnetwork is “attaching.” During “attaching,” a mobile unit which hascommissioning information contacts a router already on the mesh network,exchanges link configuration information, and finally joins the meshnetwork.

As described above herein, the network containing the mobile units canbe a mesh network. In these embodiments, devices on the network canforward messages for other devices. In some embodiments, mobile unitscan forward messages to other devices on the network, including othermobile units. In other embodiments, only dedicated relay stations canforward messages from other devices on the mesh network. If theconnection between devices acting as routers or between a mobile unitsending a message to a router becomes unreliable, then the mesh networkwill forward the message through another router in the network. A devicerouting table is maintained by one of the routers on the mesh network tomake sure that all routers have up-to-date information on possiblemessage paths for the other routers in the network. Included in theinformation regarding message paths is the “cost” of routing informationfrom each router device to every other router device in the network. Thebetter the quality of the link between a device on the network and aneighboring device is (measured by Received Signal Strength Indicator,or RSSI), the lower the “cost” of transferring messages one way or theother between the devices is. Each routing device calculates the “cost”of messages to and from its neighbor and provides this information tothe mesh network. The cost of sending a message from one router toanother in the network is therefore the sum of the costs of sending amessage over each link on the path from one router to the other router.The costs of delivering messages within the network are updated on aregular basis, so the devices within the network always have up-to-dateinformation on which path is the most efficient path on which to send amessage.

FIG. 60 illustrates another embodiment using a mesh network 6000. Inthis embodiment, the mesh network comprises a bridge device 6010,network relay stations 6020, and mobile units 6030. In this embodiment,mobile units 6030 act only as end units within the mesh network 6000.They do not forward messages from other devices on the mesh network.Rather, they only send response messages to relay stations 6020. In thisembodiment, the relay stations 6020 act as routers and within thenetwork. Their function is to forward messages from mobile units 6030 onthe network to other relay stations 6020 on their way to the bridgedevice 6010. They can also facilitate the joining of new mobile units tothe mesh network. The mobile units 6030 are connected to the relaystations 6020 within the network, which are then connected to otherrelay stations within the network. Some relay stations 6020 areconnected directly to bridge device 6010. The bridge device 6010functions as a border router, connecting directly to networks externalto mesh network 6000.

In some embodiments, the event venue will have multiple mesh networksactive simultaneously in different areas of the venue. In theseembodiments, the coverage areas of at least some of the mesh networkswill overlap with each other. If a mobile unit moves out of range of onenetwork, the mobile unit will be able to automatically join a differentmesh network.

Referring now to FIG. 61, there is illustrated a diagrammatic view ofone embodiment a relational database trending determination model. Thereis illustrated a first QID+RID record 6102 containing a first pluralityof UIDs 6104. Each of the first plurality of UIDs 6104 is a UIDidentifying a particular individual who answered the question andprovided the response associated with the first QID+RID record 6102.This is illustrated by showing a link to an SCID 6106 associated withthe question, linked from the QID of the first QID+RID record 6102.Similarly, the response associated with the question is shown by a linkto the answer 6108 associated with the RID of the first QID+RID record6102. With respect to the first QID+RID record 6102, the associated SCID2806 is “Gender,” denoting that the question is one asking for thegender of the responders. The associated answer 6108 is “R2:Female,”which denotes that the associated answer 6108 is the second answerchoice for the associated question, with that answer choice being the“female” option.

There is further illustrated a second QID+RID record 6110 containing asecond plurality of UIDs 6112. Each of the second plurality of UIDs 6112is a UID identifying a particular individual who answered the questionand provided the response associated with the second QID+RID record6110. This is illustrated by showing a link to an SCID 6114 associatedwith the question, linked from the QID of the second QID+RID record6110. Similarly, the response associated with the question is shown by alink to the answer 6116 associated with the RID of the second QID+RIDrecord 6110. With respect to the second QID+RID record 6110, theassociated SCID 6114 is “Political Affiliation,” denoting that thequestion is one asking for the political affiliation of the responders.The associated answer 6116 is “R3:Democrat,” which denotes that theassociated answer 6116 is the third answer choice for the associatedquestion, with that answer choice being the “democrat” option.

There is further illustrated a third QID+RID record 6118 containing athird plurality of UIDs 6120. Each of the third plurality of UIDs 6120is a UID identifying a particular individual who answered the questionand provided the response associated with the third QID+RID record 6118.This is illustrated by showing a link to an SCID 6122 associated withthe question, linked from the QID of the third QID+RID record 6118.Similarly, the response associated with the question is shown by a linkto the answer 6124 associated with the RID of the third QID+RID record6118. With respect to the third QID+RID record 6118, the associated SCID6122 is “Gender,” denoting that the question is one asking for thegender of the responders. The associated answer 6124 is “R1:Male,” whichdenotes that the associated answer 6124 is the first answer choice forthe associated question, with that answer choice being the “male”option.

There is further illustrated in FIG. 61 a plurality of associations 6126between UIDs and the QID+RID records. There is shown that UID(1),UID(4), UID(9), and UID(10) are each within both the first QID+RIDrecord 6102 and the second QID+RID record 6110. This means that theindividuals represented by UID(1), UID(4), UID(9), and UID(10) answereda gender query as “female” and also answered a political affiliationquery as “democrat.” Further, it can be seen that UID(5) answered thegender question as “male” and the political affiliation question as“democrat.” This arrangement allows for analytics to be run onparticular groups, thus forming a social measurement matrix. Forexample, the illustration in FIG. 61 shows that, out of the sixrespondents who answered “female” for the gender question, four of themalso stated that they affiliate themselves with the democratic party.This means that approximately 67% of the female respondents identifywith the democratic party. It can also be seen that, of those whoresponded “democrat,” 80% were female. It can also be seen that only oneout of 4 males answered the political affiliation question as“democrat,” meaning that only 25% of the male respondents identify withthe democratic party.

It will be understood that some female or male respondents may not haveanswered, but the analytics or queries used to pull the stored data fromthe database may be tailored to check whether UIDs that answered thefirst question chose one of the responses for the political affiliationquestion. For example, the first QID+RID record 6102 consisting offemale respondents has UID(1), UID(2), UID(4), UID(7), UID(9), andUID(10) answering as being “female.” However, UID(2) and UID (7) are notshown as answering “democrat” for the second QID+RID record 6110. IfUID(2) did not answer this particular question at all, while UID(7)chose the “republican” option, this could be taken into account whendetermining the results. For example, the results could instead statethat 80% of the females identified as “democrat,” be simply removingUID(2) from the pool and relying on a total female population of fiveinstead of six. Alternatively, the results could read as 67% of thefemales identified as “democrat,” 16.5% identified as “republican,” and16.5% as “undecided” to account for the one female who did not respond.It will be understood that these percentages are approximations and aremerely illustrative. They do not represent definitely how the systemwould round the percentages. It will also be understood that thedatabase may be configured in any way to allow for these association orsimilar associations. It will further be understood that these conceptscould be applied to any demographic group and any form of question, aswell as multiple demographics groups and multiple questions. It willalso be appreciated that the numbers used in FIG. 61 are small numbersfor demonstration purposes. The number of respondents could be anunlimited number.

Referring now to FIG. 62, there is illustrated one embodiment of anexample database report 6202 retrieved from the database in which queryand response data is stored. There is shown a title bar 6204 stating agiven name of the report. This name may be auto-generated or manuallyentered by the person running the report. There is further shown aplurality of query parameter columns 6206, labeled as “Query P1,” “QueryP2,” and “Query P3” for this example. In the last column is a resultsrow 6208. Each of the plurality of query parameter fields 6206 listsvarious query parameters that were run against the database, with eachrow of the report representing one full search.

For instance, a first row 6210 shown in FIG. 62 shows that a QID+RID fora gender questions with the answer of “female” was searched as parameter1, a QID+RID for a political affiliation question with the answer of“democrat” was searched as parameter 2, and a timestamp was searched asparameter 3. The “xx:xx” of the timestamp designates a wildcard, meaningany time of day is acceptable. Thus, the timestamp searched in thisexample limits the search to a particular day, but not a particulartime. It will be understood that the system could use any other methodof introducing wildcard characters. This provided a result of 67%,meaning 67% of female respondents also responded with “democrat.” Asecond row 6212 shows a similar query where the search provides thepercentage of respondents that affiliate with democrat who also arefemale, on a particular date (2015-10-30), with the result being 80%. Athird row 6214 provides yet another example where the search consists ofa asking for the percentage of females who answered democrat, butwithout limiting the search by a timestamp, resulting in a result of75%. A final example is shown in a fourth row 6216, where the search isfor females who chose Pepsi as their soft drink of choice, with a resultof 14.5%. Thus, this system allow for social measurements to be taken,as well as statistical extrapolation performed, as desired by the usersof the system. It will be understood that the example report shown inFIG. 62 is but one example. Other formats may be achieved depending onthe format of the report desired. Additionally, results may be inmultiple forms other than a percentage, such as raw numbers ofrespondents.

Referring now to FIG. 63, there is illustrated a flowchart depicting oneembodiment of a report generation method 6300. The method 6300 begins atstep 6302 where an individual authorized to use the system runs a queryor a series of queries against the database. These queries may havemultiple parameters to allow the results to be narrowly tailored to thedata the authorized individual desires. At step 6304, the databaseperforms the entered query or series of queries. At step 6306, thesystem outputs a report listing the results of the query or series ofqueries. The report may be similar to that shown in FIG. 62, or may bein other formats as well, as desired.

Referring now to FIG. 64, there is illustrated one embodiment of anexample seasonal database report 6402 retrieved from the database inwhich query and response data is stored. There is shown a title bar 6404stating a given name of the report. This name may be auto-generated ormanually entered by the person running the report. There is furthershown a plurality of query parameter columns 6406, labeled as “QueryP1,” “Query P2,” and “Query P3” for this example. In the last column isa results column 3108. Each of the plurality of query parameter fields6406 lists various query parameters that were run against the database,with each row of the report representing one full search.

For instance, a first row 6410 shown in FIG. 64 shows that a QID+RID fora gender question with the answer of “male” was searched as parameter 1,a QID+RID for a political affiliation question with the answer of“republican” was searched as parameter 2, and a seasonal timestamp rangewas searched as parameter 3. The seasonal timestamp range shown in thefirst row 6410 is 2015-04-05 to 2015-11-02 (Apr. 5, 2015 to Nov. 2,2015). This particular timestamp range corresponds to the major leaguebaseball (MLB) season in 2015. This provided a result of 65%, meaning65% of male respondents also responded with “republican” during the 2015MLB season. A second row 6412 shows a similar query where the searchprovides the percentage of respondents that affiliate with republicanwho also are male. The seasonal timestamp range for the second row 6412is listed as “2015 MLB Season.” This demonstrates that particularseasons may be designated as with unique identifiers which can beentered instead of requiring the user to know and enter a specific daterange for a particular season. The result of this query is shown to be78%. A final example is shown in a third row 6414 provides yet anotherexample where the search is for males who chose Coca Cola as their softdrink of choice, by using the seasonal timestamp range of “2015 MLBRegular Season,” resulting in a result of 45%. This means that thetimestamp range is that of the MLB regular season, not including anypostseason dates.

Thus, this system allow for social measurements to be taken based onseasons, as well as statistical extrapolation performed, as desired bythe users of the system. It will be understood that the example reportshown in FIG. 64 is but one example. Other formats may be achieveddepending on the format of the report desired. Additionally, results maybe in multiple forms other than a percentage, such as raw numbers ofrespondents. Further, the seasonal reporting could be applied to anytype of season, such as weather seasons, football, basketball, hockey,or other sports seasons, band touring seasons, Broadway musical seasons,or any other defined season.

Referring now to FIG. 65, there is illustrated a flowchart depicting oneembodiment of a seasonal report generation method 6500. The method 6500begins at step 6502 where an individual authorized to use the systemprepares a query or a series of queries to run against the database.These queries may have multiple parameters to allow the results to benarrowly tailored to the data the authorized individual desires. At step6504, the authorized individual enters in season or seasons, such asthose described above, in order to limit the returned results toresponses submitted during the particular season or seasons. At step6506, the database performs the entered query or series of queries. Atstep 6508, the system outputs a report listing the results of the queryor series of queries. The report may be similar to that shown in FIGS.62 and 64, or may be in other formats as well, as desired.

In the overall operation, a particular venue is initiated with a groupof attendees, of which a portion of those attendees may participate inanswering queries. Initially, the only knowledge that can be determinedis that there are a number of potential respondents. When the firstquery is posed, this can provide some information as to the makeup ofrespondents and, subsequently, the attendees in the venue. As morequestions are asked, more information can be determined about the groupas a whole with respect to the makeup thereof. This is to be comparedwith a polling system, wherein questions are asked of a random group ora group with a known makeup and it is the answers to these questionsthat provide a general trend. However, the makeup is fixed, i.e., forexample, the group may be known to the 50% associated with one politicalparty and 50% associated with a second political party and the responsesof the specific affiliates or any political party are analyzed againsteach other. By comparison, the analysis of the group or the trend thatthe group may have, i.e., political leanings, is a function of themakeup of that group, which is defined by the responses to a particularquery. As questions are asked, it can be determined what type of trendis present within a particular group of individuals. For example, anumber of conservative questions could be posed to the group and aresponse analyzed to determine whether that group trends toward theconservative or the liberal side. Another group of questions could beposed to the group and a determination made as to whether thatparticular group trended to high income tastes or to low income tastes.Thus, the trends of the group are a function of the responses of thegroup to a particular set of queries and, as these queries are made, thetrends will change, and because the particular makeup of the group willchange. This change in the makeup of the group is due to the number ofresponses that are provided, which is a direct function of the number of“eyeballs” that are glued to the communication medium through which thequeries are made. The responses are a direct indication of how manypeople are actually listing to the queries. As more and more people arelistening to the queries, the trends will be seen to change, due to thefact that the makeup has changed. It is important to note that the“makeup” is associated with the portion of the overall attendees thatare actually responding.

In some embodiments, analysis of responses to queries is used todetermine which query or media message characteristics are more likelyto trigger responses from event attendees. Turning to FIG. 66, there areillustrated representations of two presentations, presentation 6610(Presentation A) and presentation 6620 (Presentation B). In thisexample, both presentations are video advertisements for motor vehicles.Each presentation is characterized by a set of parameters (6612 forPresentation A, 6622 for Presentation B) which describe details of thepresentation. Each set of parameters indicates the type of motor vehiclein the presentation, the color of the motor vehicle, the environment thevehicle is in, and the weather portrayed in the presentation. Of course,each presentation can be parametized by any number of parameters aselements. The presence or absence of fireworks, for example, could bedistinguish one presentation from another. Each presentation isassociated with a query 6640 which be presented to the event attendeesfollowing the presentation. Query 6640 is the same query forPresentation A and Presentation B. Each presentation also has a responseset (6614 for Presentation A and 6624 for Presentation B) comprising ofthe responses by mobile unit users to the queries associated with therespective presentations. Finally, response graphs 6616 and 6626 plotthe magnitude of the number of responses in sets 6614 and 6624respectively to the queries presented after their respectivepresentations over time. By analyzing the information regarding howchanging the parameters affects the number of responses given by eventattendees, it can be determined which characteristics of a presentationare most likely to encourage active responses from the attendees. Thepresentations could be presented at the same venue at different times orat different venues.

Turning to FIG. 67, there is illustrated a flow chart showing theprocess by which parameters are analyzed to determine the best lifestyletriggers. The process starts with Start block 6702 and moves to functionblock 6704. At this point, the parameters for a presentation to bepresented are chosen. One or more of these parameters will be varied inthe future, or if the process has already occurred, is varied ascompared to a previous time the presentation was shown. Next, theprocess moves to function block 6706, where the presentation ispresented to the event attendees. The process flows to function block6708, where a query prompting a response is presented to the audience.Next, the process moves to block 6710, where query responses arereceived by the system. At block 6712, the responses are analyzed, withparticular focus on the magnitude of the number of responses at a givenpoint in real time for the presentation most previously shown. At thispoint, the process moves to decision block 6714. Here, a decision ismade as to whether or not another presentation will be shown. If so, theprocess loops back to block 6704, where parameters are again selected,this time varying at least one of the parameters as compared to the mostrecent presentation shown. If no more presentations are to be presented,then the process moves from block 6714 to block 6716, where theresponses to queries presented after each presentation are compared toeach other, with a particular focus on the number of responses receivedafter each post-presentation query. Finally, the process moves to block6718, there the results of block 6716 are analyzed to determine whichparameter choices result in the highest magnitude of query responses andare therefore the best lifestyle triggers for event attendees.

Some embodiments of the disclosure will vary only one parameter, whileother embodiments will vary multiple parameters. In some embodiments,one of the parameters varied is the time at which the presentation ispresented to the event audience. In some embodiments, the presentationwill be an advertisement. In other embodiments, the presentation will bean informative message. In other embodiments, the presentation will bean entertaining interlude. In some embodiments, the query presented willbe in the form of a question. In some embodiments, the query will promptattendees to vote for their preferred choice of something. In someembodiments, a parameter changed will be the music accompanying thepresentation. In other embodiments, a parameter changed will be thelength of the presentation or even the tempo thereof (slow or fast). Insome embodiments, a parameter changed will be the age of an actor oractress in the presentation. In some embodiments, a parameter changedwill be the ethnicity of an actor or actress in the presentation. Insome embodiments, a parameter changed will be the gender of an actor oractress in the presentation. In some embodiments, parameters will bechanged multiple times during a single event and presented to the sameevent audience.

Referring now to FIG. 68, there is illustrated a diagrammatic view ofone embodiment of recognizing an individual 202 entering a geo-fencedvenue. There is shown an individual 202 in possession of a mobile unit110. The venue has a geofence 6802 established that has a defined radiusaround the venue. Preferably, the radius of the geofence would notexceed the physical boundaries of the venue by much, if at all, so thatindividuals who are close enough to the venue, but still outside thevenue, are not recognized as entering the venue. The precise GPScoordinates defining the geofence may be established ahead of time. Amobile application saved on the mobile unit 110, such as the mobileapplication described hereinabove, may have saved within the applicationa series of geofence boundary coordinates, enabling the mobileapplication to recognize when it has entered into such a boundary. Asthe individual 202 passes over the geofence boundary, the mobile unit110 searches for its current GPS location. The mobile application on themobile unit 110 then recognizes it is within the geofence for theparticular venue, triggering the application to activate. Uponactivation of the mobile application, the individual 202 can bepresented with a screen to begin the rest of the processes describedhereinabove.

Referring now to FIG. 69, there is illustrated a flowchart depicting oneembodiment of a geo-fencing application trigger process 6900. Theprocess 6900 begins at step 6902, where an individual downloads a mobileapplication containing the GPS coordinates defining a geofence ormultiple geofences. At step 6904, the mobile unit searches for thecurrent GPS coordinates of the mobile unit. At decision block 6906, itis determining if the mobile unit is within the geo-fenced area definedby the downloaded GPS coordinates of the geofence. If the mobile unit isnot within the geo-fenced area, the process moves back to step 6904 tosearch again for the coordinates of the mobile unit. If, at decisionblock 6906, the mobile unit is determined to be within the geo-fencedarea, the process moves to step 6908. At step 6908, the mobileapplication is launched. Once the mobile application is launched, theindividual can be presented with a screen to begin the rest of theprocesses described hereinabove.

In general, a geo-fenced area can be defined by a fixed set ofcoordinates that define the boundary or by a single central GPScoordinate that just defines the venue. In the first example, this wouldrequire the mobile unit to compare its GPS coordinates with the set ofboundary coordinates to determine if it is within that boundary.However, since user must answer the ticket number as part of theprocess, all that is required is to launch the application and know thegeneral area and this can be achieved by using the latter example with asingle GPS coordinate. Thus, when the mobile unit of the user is withina predetermined range of any of a group of defined single GPScoordinates, each associated with a different venue, then theapplication will be launched. This additional information can, in analternative embodiment, be utilized to augment the unique code that isgenerated. The unique code, as described above, was defined as being acombination of the information from the ticket, i.e., the seat locationand the timestamp, but this also could include the central GPScoordinate for that venue, thus defining the venue in the unique codeassociated with that particular mobile unit.

In some embodiments, the venue at which the collection of statisticaldata occurs is the Super Bowl. The Super Bowl is the annual championshipgame of the National Football League (NFL), a professional Americanfootball league. Being the championship for one of the most popularprofessional sports leagues in the United States, the Super Bowlregularly enjoys exceptionally high television ratings when compared tomost other programs and sporting events. Since the Super Bowl is thechampionship game of the NFL, attending the Super Bowl in person can bevery expensive. It is reported that the highest face value ticket forthe 2015 Super Bowl was approximately $1,900. In fact, it is reportedthat, for the 2015 Super Bowl, the average price for a ticket on thesecond-hand market was reportedly over $4,000.

Because of the high price of tickets to attend the Super Bowl ascompared to tickets for other sports or entertainment events, thedemographics of the attendees most Super Bowl games are different thanthe demographics of attendees to regular-season NLF games, or even otherpost-season NFL games. Super Bowl attendees tend to be wealthier andbetter educated than the attendees of regular-season NFL games. For theSuper Bowl game that took place in 2007, the average visitor had ahousehold income of over $200,000 per year. Wealthy individuals are ahighly coveted target of advertisements, political messages, and othertypes of media messages. Because of this, any audience with a highconcentration of high-income individuals is an audience that advertisersand other parties wishing to deliver messages will want to reach.

Advertisers not only desire to deliver their messages to high-incomeaudiences, they often desire information about the individuals in theseaudiences, including their buying habits, political preferences, gendermake-up, ethnic make-up, ages, hobbies and even other sports interests.This means that such statistical information gathered from a high-incomeaudience like the Super Bowl is very valuable. Thus, in someembodiments, the statistical information gathering will take place at avenue in which an American football game event is being played, andwhere the average household income of the event attendees is over$200,000 per year. In general, a particular venue for a particular eventwill have associated therewith a particular set of demographics andattendee profiles, and these will be duplicative between events.

In one Super Bowl, the ethnic make-up of the attendees was as follows:73.7% white, 13.5% black, 8.5% Hispanic, 3.7% Asian, and 0.5% other.Most Super Bowl games will likely have a similar ethnic make-up. Whiteswill likely make up 70%-80% of the attendees, blacks will likely make up10%-20% of the attendees, Hispanics will likely make up 5%-15% of theattendees, and Asians will likely make up 2%-10% of the attendees.Attendees to the Super Bowl may also generally not live close to thevenue the Super Bowl is being held. For example, in one Super Bowl, onlyabout 25% of attendees lived near the venue, while around 75% ofattendees did not, calculated from 799 valid cases.

TABLE 1 STATES OF RESIDENCE OF VISTORS TO A SUPER BOWL Maryland 25-35%California 21-31% Texas 7.5-15%  Louisiana  7-15% Florida  3-10% Other30-40%

TABLE 2 INTERNATIONAL VISITORS TO A SUPER BOWL Canada 10-20  Mexico10-20  Australia 1-10 Brazil 1-10 England 1-10 Mexico 1-10

TABLE 3 PERCENTAGE OF PEOPLE STAYING OVER NIGHT AT A SUPER BOWL Yes70-80% No 25-35%

TABLE 4 OVERNIGHT VS. DAYTRIPPER A SUPER BOWL Daytripper Overnighter25%-35% 65-75%

TABLE 5 NUMBER OF NIGHTS STAYED IN VENUE'S CITY One Night  3-10% TwoNight 15-25% Three Night 25-35% Four Night 30-40% Five Nights or More20-30% Average # of Nights 3.6 Median # of Nights 4.0

TABLE 6 TYPE OF LODGING USED BY VISTORS TO A SUPER BOWL Hotel 70-80%Friends or Relatives 15-25% Private Home Rental 2-5% RV 2-5% Bed andBreakfast 1-5% Other 0.5-2%  

TABLE 7 NUMBER OF HOTEL ROOMS USED BY VISTORS TO A SUPER BOWL One Room75-85% Two Rooms 15-25% Three Rooms 3-8% Four Rooms or More  4-10%Average # of Rooms 1.7

TABLE 8 TRANSPORTATION USED BY VISITORS TO THE SUPER BOWL Airplane60-70% Personal Vehicle 30-40% Other  5-10%

TABLE 9 PRIMARY PURPOSE OF VISIT TO VENUE LOCATION Super Bowl 95-99%Other Vacation/Pleasure 1-5% Business/Convention 0.1-2%   Other 1-3%

TABLE 10 AVERAGE INDIVIDUAL DAILY EXPENDITURES OF SUPER BOWL VISITORSNational Football Overnight Regular Day- Other League and its VisitorTripper Day-Tripper Entitties $575 $680 $402 $718

TABLE 11 PARTICIPATION BY ATTENDEES IN SUPER BOWL ACTIVITIES Sunday -Super Bowl Game 70-80% Saturday - NFL Experience 30-40% Saturday - SuperBowl Boulevard 25-35% Sunday - Super Bowl Boulevard 18-25% Friday -Super Bowl Boulevard 15-25% Thursday - NFL Experience 12-20% Friday -NFL Experience 10-20% Wednesday - NFL Experience 10-20% Thursday - SuperBowl Boulevard  5-10% Sunday - NFL Experience 3-8% Tuesday - Media Day2-6% Saturday - Super Saturday of Service 0.5%-2%   

TABLE 12 NUMBER OF PEOPLE IN PARTIES OF SUPER BOWL ATTENDEES One person0%-10% Two people 40%-60%  Three people 10%-20%  Four people 10%-20% Five people 5%-15% Six people 0%-10% Seven people or more 0%-10%

TABLE 13 SUPER BOWL ATTENDEES WITH CHILDREN IN THEIR PARTIES No children55%-65% One child 15%-25% Two children 10%-20% Three children  0%-10%Four children  0%-10% Five children or more  0%-10%

TABLE 14 ANNUAL INCOME OF SUPER BOWL ATTENDEES Less than $25,000  0%-10% $25,000-$49,000  0%-10%  $50,000-$74,000  5%-15%  $75,000-$99,00010%-20% $100,000-$149,000 15%-25% $150,000-$199,000 10%-20% $200,000 andabove 15%-25%

TABLE 15 ETHNICITY OF SUPER BOWL ATTENDEES White 65%-80%  Black 10%-20% Hispanic 5%-15% Asian 1%-10% Other 0%-10%

TABLE 16 GENDER OF SUPER BOWL ATTENDEES Male 60%-70% Female 30%-40%

TABLE 17 AGE OF SUPER BOWL ATTENDEES 18-24 years old  5%-10% 25-34 yearsold 20%-30% 35-49 years old 35%-45% 50-64 years old 20%-30% Older than65 years  5%-10%

Referring now to FIG. 70, there is illustrated a diagrammatic view ofone embodiment of a Predictive Consumer Trends Engine neural network7000. Neural networks are non-parametric methods used for patternrecognition and optimization. They are able to generate an output basedon a weighted sum of inputs, which is then passed through an activationfunction. Typically, the activation function determines the output bysumming the inputs multiplied by the weights. A basic activationfunction is that of y=ƒ(Σwx), where x is the vector of inputs, w is thevector of weights, ƒ(.) is the activation function, and y is the outputvector. It will be understood by those skilled in the art thatvariations on the activation function may be used or represented inother ways, such as the activation function: a=Σ_(i=0) ^(i=n)W_(i)X_(i).Yet another activation function is the softmax activation function,which is generally used for probabilities. The softmax function is:

${f(x)} = {\frac{e^{x}}{\sum\; e^{x}}.}$

The inputs, weights, and outputs may be organized within a multilayerperceptron (MLP), wherein there is an input layer, one or more hiddenlayers, and an output layer. As shown in the network 7000, a pluralityof inputs 7002 reside in the input layer, a plurality of neurons 7004(the weights) reside in the hidden layer or layers, and a plurality ofoutputs 7006 reside in the output layer. It will be appreciated that theneural network 7000 may contain any number of inputs, neurons, andoutputs, from 1 to n. Thus, this creates a feedforward network. Afeedforward network, as shown in FIG. 70, is called such because theinputs in the input layer feed into each of the neurons in the hiddenlayer or layers, which in turn feed into each of the outputs in theoutput layer, with each output in the output layer providing a result.It will be understood by those skilled in the art that the neuralnetwork would then be trained in order for the neural network to becomemore accurate. Various training methods exist, such as supervisedlearning where random weights are fed into the neural network andadjusted accordingly, backpropagation methods, or other methods.Further, a bias might be introduced to avoid problems associated with athreshold value as the neural network evolves. Activation functions areapplied to the weighted sum of the inputs to generate a certain outcome.Neural networks typically take a non-linear form. Consequently, sigmoidfunctions are often preferred for their continuous character. Varioussigmoid functions may be used, such as

${\left( {{f(x)} = \frac{1}{1 + e^{- x}}} \right)\mspace{14mu}{or}}\mspace{14mu}$$y = \frac{1}{1 + e^{{- a}/p}}$where a is the activation into the neuron and p is a number whichcontrols the shape of the curve, where p is usually set to 1.0. Thelearning may then include several training cycles, such as passingthrough the model a set of m input-output pairs (x^(d), ƒ^(d)), d=1, . .. , m, which form the training sample, and adjusting the weights todecrease the value of the deviations of the model outputs, y^(d), fromthe target values, t^(d). The weights may be set to small random valuesinitially. The input pattern may then be applied and propagated throughthe network until a certain output is generated for the hidden layer. Anequation such as h_(j) ^(d)=ƒ(net_(j) ^(d))=ƒ(Σ_(k)w_(jk)x_(k) ^(d)),where d=1, . . . , m is the number of input-output pairs available inthe training set, is the output of the hidden unit j, net_(j) ^(d) isthe input of the hidden node j, x_(k) ^(d) is the input node k, W_(jk)is the weight hive to input k for hidden node j, and ƒ(.) is theactivation function used in the hidden layer.

The outputs of the hidden layer, h_(j) ^(d), are then used as entriesfor the output layer. Weighted and summed up, they are passed through anactivation function to produce the final output: y_(i) ^(d)=g(net_(i)^(d))=g(Σ_(j) w_(j)h_(j) ^(d))=g(Σ_(j)w_(ij)·ƒ(Σ_(k)w_(jk)x_(k)^(d)))_(p) where y_(i) ^(d) is the exit value of the output unit i,net_(i) ^(d) is the input of the output node i, w_(ij) is the weightgiven to the hidden node j for the output node i, and g(.) is theactivation function used in the output layer. The way the weights aremodified to meet the desired results defines the training algorithm andis essentially an optimization problem. When the activation functionsare differentiable, the error back-propagation algorithm may be a goodapproach in progressing towards the minimum of the error function, E(w).The form of the function may be:

${E(w)} = {\frac{1}{2}{\sum\limits_{d = 1}^{m}\;{\left( {t^{d} - y^{d}} \right)^{2}.}}}$For two output nodes (I={1, 2}), the error becomes:

${E(w)} = {\frac{1}{2}{\sum\limits_{d = 1}^{m}\;{\sum\limits_{i = 1}^{2}\;{\left( {t_{i}^{d} - {g\left( {\sum\limits_{j}{w_{ij} \cdot {f\left( \;{\sum\limits_{k}\;{w_{jk}x_{x}^{d}}} \right)}}} \right)}} \right)^{2}.}}}}$The errors are then passed back through the network using the gradient,by calculating the contribution of each hidden node and deriving theadjustments needed to generate an output that is closer to the targetvalue. For the hidden to output, the gradient is computed, such as:

${\Delta\; w_{ij}} = {{{- \eta}\frac{\partial E}{\partial w_{ij}}} = {\eta{\sum\limits_{d = 1}^{m}\;{\left( {t_{i}^{d} - y_{i}^{d}} \right){{g^{\prime}\left( {net}_{i}^{d} \right)} \cdot {\left( h_{j}^{d} \right).}}}}}}$For the input to hidden connections, the gradient may also be computed,such as:

${\Delta\; w_{ij}} = {{{- \eta}\frac{\partial E}{\partial_{jk}}} = {{{- \eta}{\sum\limits_{d - 1}^{m}{\frac{\partial E}{\partial h_{j}^{d}} \cdot \;\frac{\partial h_{j}^{d}}{\partial w_{jk}}}}} = {\eta{\sum\limits_{d - 1}^{m}{\sum\limits_{i - 1}^{2}\;{\left( {t_{i}^{d} - y_{i}^{d}} \right){{g^{\prime}\left( {net}_{i}^{d} \right)} \cdot w_{ij} \cdot {f^{\prime}\left( {net}_{j}^{d} \right)} \cdot {\left( x_{k}^{d} \right).}}}}}}}}$In calculating these gradients, n is the learning rate which controlsthe size of the step in each cycle, g′(.) is the first order derivate offunction g(.), ƒ′(.) is the first order derivate of function ƒ (.). Thenew weights can then be adjusted taking also into account themodification from the previous cycle, this method being calledback-propagation with momentum rate. This parameter is used to speed upthe convergence process in flat regions, or to diminish the jumps inregions of high fluctuations, by adding a fraction of the previousweight change, using, such as:

${\Delta\;{w\left( {n + 1} \right)}} = {{{- \eta}\frac{\partial E}{\partial w}} + {a\;\Delta\;{{w(n)}.}}}$

It will be understood by those skilled in the art that neural networkscan be set up and trained in various ways and that the above descriptionis illustrative of but one method. It will be appreciated that theneural network may be organized in any way to allow for thefunctionality disclosed herein.

It will be understood by those skilled in the art that the data to beused as inputs according to the system described herein consists largelyof nominal values, e.g., gender={male, female}, ethnicity={white, Asian,Latino, African American}, and so on. Non-numeric data such as thismakes creating a neural network more challenging, as neural networks cantypically only process numeric inputs and provide numeric outputs.However, there are ways of utilizing nominal values in neural networks.One method is to assign each category within a variable a numeric value.For example, if the variable is “gender” then male=1 and female=2. Thismethod works well for variables that have a binary result like “gender.”However, this method causes a neural network to not perform as well ifthere are variables with more than two categories, such asethnicity={white, Asian, Latino, African American}. When such is thecase, it is typically more beneficial to apply ratings to the variablesor to the categories based on known information. For instance, if thequestion to be determined from the data is whether consumers aretrending towards purchasing a Ford truck, and past data has beenaccumulated indicating that gender plays a large role in the decision topurchase a Ford truck, the gender variable input may been given a weightof 0.95 for example (on a scale from 0 to 1). Similarly, this conceptcould be applied to categories within a variable. If, for instance, pastdata indicates that the male gender has a strong correlation withpurchasing a Ford truck, while females do not, then, for instance, theinputs could be set to male=0.95 and female=0.05. This concept could beapplied to any other variable or category. This known data may play animportant role in training the neural network because, given thatcertain relationships may already be known based on past dataaccumulated, such as from responses to queries, the neural network maybe fed inputs with the weights being adjusted until the output oroutputs resemble the expected output or outputs.

Referring now to FIG. 71, there is illustrated a diagrammatic view ofanother embodiment of a Predictive Consumer Trends Engine neural network7100. There exists in the input layer four inputs 7102, in the hiddenlayer two neurons 7104, and an output 7106 in the output layer. Thisserves as an example of one way the Predictive Consumer Trends Enginemay be organized to provide for consumer trends to be predicted. Thisembodiment envisions a single output, which may be in the form of abinary result, percentages, or other results. For instance, if one wouldlike to predict how people of a certain demographic feels aboutpurchasing a Ford truck, the four inputs 7102 could be tailored tospecific demographics, such as gender, ethnicity, income level, and soon, with a single output. The single output may be a binary answer(yes/n or 0/1) or it could be a percentage indicating the likelihoodthat the given demographics would choose to purchase a Ford truck.

Additionally, the embodiment shown in FIG. 71 is based on one method ofdetermining the number of neurons to be used in the neural network.Although there could be any number of hidden layers, typically rangingfrom one to three, it will be appreciated by those skilled in the artthat a single hidden layer can estimate differentiable functions,provided there are enough hidden units. A higher number of hidden layersalso increases processing time and the amount of adjustments neededduring neural network training. One method of determining the number ofneeded neurons in the hidden layer is represented by: N_(h)=√{squareroot over (N_(i)·N_(i))}, where N_(h) is the number of hidden nodes,N_(i) is the number of input nodes, and N_(o) is the number of outputnodes. Thus, as shown in FIG. 71, since there are four inputs and oneoutput, the number of neurons is N_(h)=√√{square root over(4·1)}=√{square root over (4)}=2. Thus, there are two neurons in theneural network of FIG. 71. It will be appreciated that the number ofneurons will change depending on the number of inputs and outputs.Further, the method for determining the number of neurons may also bedifferent, as this is but one example.

Referring now to FIG. 72, there is illustrated a diagrammatic view ofone embodiment of a Predictive Consumer Trends Engine 7200. The Engine7200 is illustrated in a manner that is indicative of how an end userwould utilize the fully-trained engine 7200. That is, the end user mayonly be concerned with the inputs being fed into the engine 7200 and theoutput generated, with the inner-workings of the hidden layer beingunimportant to the end user. There is illustrated a plurality of inputs7202 being fed into a trained predictive engine 7204, with an output7206 being generated by the trained predictive engine 7204. The inputsfed into the trained predictive engine 7204 may, as described above, benumerical values representing a plurality of consumer demographiccategories, with each category represented by a numerical rating givento the category before-hand due to known information. For instance, ifit is to be determined by the predictive engine 7204 the likelihood of aconsumer purchasing tickets to a National Football League (“NFL”) game,the male gender might have a rating of 0.90, while female has a ratingof 0.10. This format could be used for every known or used demographiccategory. For instance, an income level between $15,000 and $30,000might have a rating of 0.08, an income level between $50,000 and$100,000 might have a rating of 0.15, and an income level between$100,000 and $250,000 might have a rating of 0.75. It will beappreciated by one skilled in the art that the number of categories canbe any number.

It will also be appreciated by one skilled in the art that the end user,if supplying inputs with only the known and previously applied ratings,may only generate a known output. That is, the output 7206 would be aresult that should already be known since the data would represent thepresent-day and current ratings that indicate how the categories affectthe result to be reached. However, if the end user wishes to predictfuture consumer trends, the end user may change the previously appliedratings by how demographic changes might change the ratings. Forexample, if there is a suspicion that the male population may experiencea substantial increase in the coming years, the end user may adjust themale rating to have even more of an impact on whether consumers will buytickets to an NFL game, such as increasing the rating to 0.95. Thus,this allows the predictive engine to make predictions on future trendsbased on past data and changes in the market. The plurality of inputs7202 may also include raw numbers, so that, in addition to ratings forcategories, other categories that could be represented by raw numbers,such as the number of males in the United States or any other locale.

Referring now to FIG. 73, there is illustrated a flowchart depicting oneembodiment of a predictive consumer trends engine method 7300. Themethod 7300 starts at step 7302, where a consumer trend to be predictedis determined. The trend may be anything, from preferred grocery storeto whether consumers will purchase Latin music recordings. The methodthen moves to step 7304, where there is provided a plurality of inputshaving known qualities based on past data. The plurality of inputs maybe the demographic categories as described hereinabove. The categorieswould have associated numerical data based on past and current trends.At step 7306, a predictive consumer engine trained on the past andcurrent data is provided. At step 7308, the plurality of inputs isadjusted in order to provide for a change in the past and current trendsdata. For example, if it is known that a certain ethnicity is growing inthe United States, the input for this ethnicity could be altered toreflect that change. At step 7310, the plurality of inputs, nowadjusted, is fed into the trained predictive consumer engine. Theprocess then ends at step 7312, where the engine generates an outputthat is the prediction of the consumer trend.

Referring now to FIG. 74, there is illustrated a diagrammatic view of atraining database 7402. The training database is a repository ofcollected data relating to the various inputs that can be derived fromthe generated database of the present system, as described hereinabove.For example, results can be collected from individuals regarding suchthings as their political affiliation, their gender, their demographics,their age and their income, the age and income being divided intodifferent brackets. For example, the age of an individual could bebracketed as 18-25, 26-35, 36-45, etc. and income could be bracketed$0-$40,000, $41,000-$60,000, $61,000-$100,000, etc. There will also be aset of polling data and the results of that polling data that will beassociated with each individual, this comprising a first data set. As anumber of individuals are interviewed with the various pollingquestions, new data sets are created and stored in the database 7402.The polling question could simply be their preference of cars, i.e.,Ford, Chevrolet, Mercedes, etc., with one selected for a particularindividual. Once the entire training database has been created, a trendwill exist within the data with respect to the makeup of a particularpolitical affiliation with respect to all of information collected fromthe individuals. This polling data could also be directed to such thingsas the type of cars people like, the types of vacations they like, etc.Any type of polling question could be provided which answer can beassociated with that individual to define a data set for thatindividual.

Referring now to FIG. 75, there is illustrated a diagrammatic view of apredictive engine 7502 being trained with the information in thetraining database 7402. The predictive engine will have a plurality ofinputs defined as an input data vector and a plurality of outputs ofdefining the output data vector. The predictive engine 7502 two providesa representation of the input data map therethrough to provide an outputdata. Thus, if the predictive engine 7502 were trained for the pollingquestion “type of car you purchased in the last year,” each data setwould be input to the output of the predictive engine 7502, for a neuralnetwork example, and back propagation utilized to train the predictiveengine 7502 for all the data sets in the training database 7402. Thiswould provide a representation of the trends embodied within thetraining database 7402. Once trained, all of the weights in thepredictive engine 7502 (assuming this is a neural network) provide atrained predictive engine.

Referring now to FIG. 76, there is illustrated a diagrammatic view of atrained predictive engine 7602, which is the predictive engine 7502after training. In this mode, a real-time database 7604 is utilized,which contains currently collected data that has not been associatedwith any polling data. For example, if the individuals upon which datawas collected in the database 7604 had never had any informationassociated with what type of car they purchased in the last year or anytype of information regarding what type of car they would prefer, theinput collected on the individuals to be input to the trained predictiveengine 7602 may be any predicted trend as to that particular resultprovided. Of course, it should be remembered that trained predictiveengine 7602 will only output predictions as to what it was trained on.Thus, if the predictive engine 7502 described hereinabove with respectto FIG. 75 was only trained on type of car, that would be all that wasrepresented by the train predictive engine 7602. However, during thetraining procedure, other polling results could be utilized in thetraining to provide multiple vector outputs. This, again, could beanything such as lifestyles, vacation trends, etc. that could beutilized as a trend of a particular population.

The embodiments of the systems and methods described herein and may beutilized by live event organizations and venues for their own purposeson a local level, such as running reports, analytics, or predictingtrends, as described herein. As described with respect to FIG. 1, theremay be a local CO 120 associated with a venue 102. The local CO 120 mayhave a computer or other device connected with a local database 122, thelocal database 122 storing all query and response data for the venue. Insome embodiments, the local database 122 stores all query and responsedata during a live event, and only transmits this data to the centralremote office 126 after the conclusion of the live event. However, thelocal database 122 would still have stored a copy of all of this data.Thus, this creates an opportunity for the venues, or organizationsassociated with the venues, to use this data for their own purposes. Theentities that would have access to this data may be the company thatowns the venue (real estate) itself, sports teams who are own or areaffiliated with the venue, sports organizations affiliated with theteams that play at the venue, or any other entity that has a stake inthe operation of the venue, depending on how the rights are agreed upon.For example, the Dallas Cowboys football stadium might allow access tothe data to both the Dallas Cowboys organization and the NFL. However,they would only have access to data specific to this venue (or othervenues under the organization's umbrella) that is saved on the localdatabase 122. These organizations would not have access to all dataaccumulated on the central database 126, as the data on the centraldatabase 126 may include data from other venues owned by otherorganizations. For example, the NFL may not have access to the MLB data.Thus there is provided an incentive to these organizations to use theproduct that will provide the features disclosed herein, as the datawill be useful to these organization in studying their own fan base,while still providing all data from all venues to the entity owning thecentral remote office 126.

Referring now to FIG. 77, there is illustrated a flowchart depicting oneembodiment of a social analytics generation utilizing local serversmethod 7700. The method 7700 starts at step 7702 wherein an organizationassociated with a venue attempts to access a local database associatedwith the venue. This local database may be the local database 122 asshown in FIG. 1, or it may be any other database the organization isusing for storage of data gathered from live events. At decision block7704 it is determined whether the attempt by the organization associatedwith the venue is a verified access. If it is not, the method moves backto step 7702, where another attempt to access the local database may bemade. If it is verified, the method moves to step 7706. At step 7706,the organization associated with the venue performs reports, analytics,trends predictions, or other data analysis, such as that describedherein, on the data stored on the local database. At step 7708, a reportor other output is generated for use by the organization associated withthe venue. It will be appreciated that this allows the organizationassociated with the venue a unique way of tracking attendees to thevenue in a way that other audience tracking systems, such as thatprovided by Nielson, do not provide. This is because the systems andmethods disclosed herein allow for tracking of guaranteed viewers. Otheraudience tracking systems, such as that provided by Nielson, only recorda small number of the overall audience and extrapolate from there.However, the systems and methods disclosed herein allow typicallyrequire the participation of audience members, e.g., if a query is beingresponded to, the exact number of responses can be tracked as well asdata accumulated on those whose responses are being recorded. Thisprovides a much more detailed and narrow focus on audience analytics tobe achieved that other services do not provide.

Turning to FIGS. 78A-78D, in some embodiments, responses to queries areanalyzed in real-time, as opposed to analyzing responses only after thetime window to respond to a query has closed. Doing so allows eachresponse to be time-tagged as to when it was either created or received,rather than analyzing the final results of a query, wherein theresponses as a while would only be time-tagged to the window or query.In FIG. 78, there are illustrated a number of graphs, FIGS. 78A, 78B,78C, and 78D. These graphs depict the number of responses to a queryreceived since the beginning of the query's time window as a function oftime of generation or receipt. The horizontal axis 7850 of each graph isthe “time” axis, and depicts the time that has elapsed since thebeginning of the time window for a particular query, with the elapsedtime increasing in the positive direction of the axis. The vertical axis7860 of each graph depicts the accumulated number of responses receivedfor a query during its time window. In the example depicted in FIG. 78,the query in question has three possible responses, represented by “A,”“B,” or “C.” Each graph of FIG. 78 depicts a different set of responses.The graph of FIG. 78A labeled “TOTAL RESPONSES FOR QUERY” depicts thetotal accumulated number of responses received for that query up to agiven period of time, while FIG. 78B depicts only the number ofrespondents responding with choice “A,” FIG. 78C depicts only “B”responses, and FIG. 78D depicts only “C” responses.

“Real-time data” refers to the concept that information regardingresponses to a query can be viewed and analyzed during and before theclosing of the time window. The information is updated at somepredetermined frequency, which varies by embodiment. For example, insome embodiments, information regarding responses is updated once everysecond. In other embodiments, the information is updated every fiveseconds. In some embodiments, the update rate can vary throughout thetime window. “Real-time data” also refers to the concept that historicaldata can be viewed as a function of the time at which responses werecreated/received. In some embodiments, real-time data is saved and canbe viewed and analyzed after the closing of a query's time window.Real-time data is useful, not only because it not only allows foranalysis of the final, post-time-window information regarding responses,but also because it allows for the analysis of when responses aregenerated/submitted. This information is useful in situations, forexample, when a query is presented which is related to a video messagethat plays during the time window for responses to the query. Knowingwhen and how users respond to the query will help determine how thevideo, and which parts of the video, have the greatest effect on theresponding tendencies of attendees.

The number of responses received up to a given point can be measured invarious ways. For example, in some embodiments, the total number ofresponses is measured as an accumulated number. In other embodiments,the number of responses for each response choice is measured, i.e., forthe response A, B, or C. In some embodiments, responses are measured asgrouped by demographic, location within the event venue, or some otheridentifying information. For example, the total number of femalesresponding could be measured as a function of time, or the total numberof people in the age range of 28-35 who have responded could be measuredas a function of time.

Turning to FIGS. 79A-79D, there are illustrated examples of how responserates (in number of responses generated/submitted) can be analyzed. Insome embodiments, the rate at which responses to queries are received isanalyzed in real-time. In these embodiments, the rate at which responsesare received is analyzed as a function of time. In FIG. 79, there areillustrated several example graphs FIGS. 79A, 79B, 79C, and 79Ddepicting the rate of responses to a query. In each of these graphs, thehorizontal axis represents the time that has elapsed since the beginningof a query time window, while the vertical axis represents the rate ofresponses being received. In some embodiments, such as that depicted inFIGS. 79B-D, the rates of responses are grouped by the type of response.For example FIG. 79A represents the rate of all of the responses to thequery received, while FIG. 79B represents the rate of responsesselecting “A” that are received, FIG. 79C represents the rate ofresponses selecting “B” that are received, and FIG. 79D represents therate of responses selecting “C” that are received. The informationpresented in these graphs allows the determination of which part of thetime window had the highest rate of responses. For example, in someembodiments, a video message plays during the query time window. Knowingwhich part of the video message elicited the highest rate of responsesat a particular point in time—in total, by response choice, or amongvarious demographic groups—would prove useful for gathering informationon what viewers respond to.

As with the real-time analysis of the number of responses, the rate ofresponses can be measured in various ways. In some embodiments, the rateof total responses is measured, while in some embodiments, the rate ofeach response choice is measured. In some embodiments, the rate ofresponse is measured by demographic group or location within the eventvenue.

Referring to FIGS. 80A-E, there is illustrated the real-time measurementof responses by location. In some embodiments, real-time measurements ofresponse numbers or rate of response are measured for multiple locationswithin the event venue. The measurements for each location can then becompared against each other and against the measurements for the entirevenue. In FIG. 80, there is illustrated a diagram of an overhead view ofa stadium 8010. In this example, the stadium is divided into foursections 8012, Sections I, II, III, and IV. Also illustrated in FIG. 80are four graphs 8020. Each graph corresponds with a section of thestadium and depicts the real-time measurements of the rate of responsesin the section. For example, the graph of FIG. 80B labeled “I” depictsthe rate of responses from Section I in the stadium as a function oftime, the graph of FIG. 80C labeled “II” depicts the rate of responsesfrom Section II in the stadium as a function of time, the graph of FIG.80D labeled “III” depicts the rate of responses from Section III in thestadium as a function of time, and the graph of FIG. 80E labeled “IV”depicts the rate of responses from Section IV in the stadium as afunction of time. In some embodiments, the responses are groupedaccording to where the user's seat is located, and any responsessubmitted by users while not in their seats are outliers. Gatheringreal-time data grouped by respondents' locations allows the systemoperator or venue operator to understand how attendees' locations affecttheir response choices or whether or not they respond at all. If, forexample, Section I of the stadium 8010 has a large video screen on whichvideo messages are played, then a low response rate from Section I mightindicate that the attendees in Section I are too close to the videoscreen to see the video message. As another example, if a video messageprompting attendee responses and viewable only by Section II ispresented with a corresponding audio message heard by the entirestadium, then comparing the real-time responses of Section II with therest of the stadium will allow the venue operators to understand thedifferences in how audio and video messages, and what parts within eachmessage, prompt attendees to respond to queries.

Referring to FIG. 81A-D, there are illustrated examples of how real-timemeasurements can be grouped by demographic group. FIG. 81A depicts therate of responses to a query as a function of time. The other graphs,FIGS. 81B-D, depict the rate of responses, but each of FIGS. 81B-Ddepicts the real-time data broken down by the categories of a certaindemographic class. Attendees are broken down into demographic groups onthe basis of previously gathered user information. For example, FIG. 81Bdepicts the rate of responses broken down by gender. One line on FIG.81B depicts the rate of responses by users likely to be males, and theother line depicts the rate of responses by users likely to be females.Similarly, FIG. 81C depicts rate of responses, except that graph FIG.81C is broken down by likely political affiliation, with one line forRepublicans, one line for Democrats, and one line for independents.Lastly, FIG. 81D is an example of a graph depicting the rate ofresponses as a function of time, but broken down by the likely age ofthe responding users. Like the embodiments which break down the rate ofresponses by the choice of response, the embodiments depicted in FIG.81A-D allow system operators and venue operators to better understandwhat parts of messages resonate best with a particular demographicgroup. Other embodiments will have similar graphs breaking down users byother demographics, including income level, race or ethnicity, preferredsports team, or any other demographic useful to advertisers or thoseseeking information about consumers. In some embodiments, eachdemographic will be plotted on the same graph axes, while in otherembodiments, each group will have its own set of axes.

Turning to FIG. 82, there is illustrated an example of a screen forselecting how to view real-time data regarding responses and rates ofresponses. In some embodiments, system operators will be able to selectif and how real-time data is broken down by demographics when it ispresented. Menu 8210 is a graphical menu that would appear on the screenof a computer, mobile phone, or any other device a system operator woulduse to analyze real-time data. This menu has several check-boxes 8220,each associated with a certain demographic class, such as gender,political affiliation, age, etc. By selecting a check-box, the systemadministrator causes the system to present real-time measurement databroken down by the demographic associated with that check-box. In theexample depicted in FIG. 82, the check-box associated with age isselected, meaning that real-time measurement data would be presented ina form that shows the measurements as a function of time for various agegroups, in a manner similar to the graph of FIG. 81D. If none of theboxes are selected, then only the information for all respondents as awhole is presented. In some embodiments, the selection of whichdemographic class to group responses by can be changed while the timewindow for query responses is still open, thus changing whatdemographics are presented even before all of the data has beencollected for the time window. In some embodiments, the selections willalso include response choices, such that real-time data can be viewedfor each response choice. In some embodiments, after a time window hasended, historical data can still be viewed as a function of time.

Turning to FIG. 83, there is illustrated an embodiment in which systemoperator uses real-time measurements to select a subsequent mediamessage. Depicted in FIG. 83 is display 8310, which, in someembodiments, is what is displayed on the screen of a computer, mobiledevice, or any other device that could be used by a system operator. Inthis example, a query is presented that corresponds with a video messagewhich plays during the time window for query responses. Video message8320 is an area of the display which plays the same video message thatis presented to the event attendees. In some embodiments, the videomessage 8320 is presented simultaneously with the message played toevent attendees, while in others, the video message is played after thetime window for responses has ended. Graphs 8330 show real-timemeasurement data as a function of time, and in some embodiments, as inthe one depicted in FIG. 83, the graphs show real-time measurementsbroken down by demographic. Other embodiments will have graphs brokeninto groups by response choice. In FIG. 83, the graphs 8330 show therate of response broken down by gender. In some embodiments, the data ispopulated on graphs 8330 at the same rate as the video message 8320plays. In this way, the system operator sees exactly what part of thevideo message corresponds with a particular point in the real-time data.Time indicators 8340 display the time within the video and the time forwhich real-time data is being populated in the graphs 8330. In someembodiments, the same display 8310 as depicted in FIG. 83 also includesselection options 8350. These selection options allow the systemoperator to choose the next media message to be presented. The systemoperator can use the real-time measurement information to help decidewhich message is the best one to be played next. In the example of FIG.83, there are three options 8350, one for a male-oriented message, onefor a female-oriented message, and one for a gender-neutral message. Byevaluating the real-time measurements, the operator can determine whichof the three options is best. Selecting one of the options 8350 willcause the message corresponding to that option to be presented at somelater time. In other embodiments, other selection options will bepresented. In some embodiments, the options are all for differentsubclasses of a demographic classification, such as gender, age, incomelevel, political affiliation, or sports team preference. In otherembodiments, the selection options will be for messages featuredifferent products or services.

In addition to viewing data in real-time, some embodiments allow data tobe presented back to audiences in real-time. For example, if a query ispresented to attendees at a live event, then the results can bepresented to the attendees on a video screen while the response timewindow is still open. The results on the video screen can becontinuously updated throughout the time window as more real-timemeasurements are collected. This allows attendees to view how theirresponses compare to other attendees. In some embodiments, the real-timemeasurements are automatically presented back to attendees, while inothers embodiments, the measurements are only presented when the systemoperator chooses to do so. In some embodiments, historical real-timemeasurements are presented back to the audience after a time window hasended.

Turning to FIG. 84, there is illustrated a flowchart detailing theprocess that occurs during the use of a program such as the onedescribed and depicted in FIG. 83. The process starts with Start block8402 and proceeds to decision block 8404. If the query time window isnot open, the process loops back to just before decision block 8404. Ifthe query time window is open, the process flows to decision block 8406.If real-time measurement data is not being collected, the process endsat block 8408. If real-time measurement data is being collected, theprocess flows to decision block 8410. At decision block 8410, if thedesired data to be viewed is the accumulated number of responses, theprocess moves to block 8412, where the accumulated number of responsesis displayed. If, at block 8410, the desired data is response rate data,the process moves to block 8414, where the response rate measurementdata is displayed as the number of responses per a unit of time. Afterthe process moves past either block 8412 or 8414, the process flows todecision block 8416. At this point, the user decides if he/she wants tofilter the real-time measurement data. If so, then the filtering processtakes place as depicted in FIG. 85 (described hereinbelow). Once thefiltering process is finished, or if the user chooses not to viewfiltered data, the process flows to decision block 8418, where the userdecides whether or not to engage in display interaction. If so, thedisplay interaction process takes place, as depicted in FIG. 86 anddescribed hereinbelow. Once that process is finished, or if the userdecides not to engage in display interaction, the process flows todecision block 8420. Here, the user has a chance to control thesubsequent path of the messages. If the user decides to do so, thesubsequent path control process takes places as depicted in FIG. 87(described herein below). Once the path control process has finished, orif the user decides not to control the subsequent message path, theprocess moves to function block 8422. At block 8422, the systemgenerates subsequent message path control information based on theinformation, if any, that resulted from the subsequent path controlprocess of FIG. 87. Next, the process flows to decision block 8424,where, if the query response time window has not ended, the processloops back to before decision block 8410. If the time window has ended,the process flows to block 8426, where the process ends.

Turning to FIG. 85, there is illustrated a flowchart depicting thefilter process that occurs in FIG. 84. If, in decision block 8416, theuser decides to view filtered data, the process moves to block 8502,where the filtering process begins. The process then moves to functionblock 8504, where the system displays the possible filter options thatcan be selected. Next, the process flows to function block 8506, wherethe user selects the desired filter options. The process then flows tofunction block 8508, where the selected filter options are applied tothe real-time data. At function block 8510, the user selects the desireddisplay mode for viewing the real-time data. Finally, the process flowsto return block 8512, where the process flows back to block 8418 fromFIG. 84.

Turning to FIG. 86, there is illustrated a flowchart depicting thedisplay interaction process. The process starts when (and if) the userdecides to begin the process at block 8418 of FIG. 84. The process movesfrom block 8418 to block 8602 and then to decision block 8604. Atdecision block 3604, the user decides whether or not to change someaspect of the displayed video message. If the user decides not to, theprocess flows to return block 8606 and back to block 8420 of FIG. 84. Ifthe user decides to interact with the message, the process moves tofunction block 8608, where the user decides which action to take relatedto modifying the message. Next, the process moves to function block8610, where the modifications are applied, and finally to return block8612, where the process returns to FIG. 84 to decision block 8420.

Turning to FIG. 87, there is illustrated a flowchart depicting thesubsequent path control function that occurs if the user decides to takecontrol of the message path in decision block 8420 from FIG. 84. In thatcase, the process flows from decision block 8420 to start block 8702 andon to decision block 8704. Here, the user has a chance to decide whetheror not to affect the message path. If the user decides not to make anysubsequent message path decisions, the process flows to return block8706 and on to block 8422 in FIG. 84. If, at block 8704, the userchooses a subsequent message path, the process flows to block 8708,where the decision is logged for later reference and use. Finally, theprocess moves to return block 8710 and back to function block 8422 fromFIG. 84.

Turning to FIG. 88, there is illustrated a diagram of how, in someembodiments, the system operator can interact with the displayed videomessage such that the video message being displayed to the audience canbe varied while the video message is actually being presented. In theseembodiments, the video message 8801 being presented to audience iscomprised of several different interchangeable video segments 8802 thatare presented one after the other. These segments 8802 are drawn from asegment bank 8804 which is comprised of all of the possible videosegments 8802 that can be used to make the video message 8801. Each ofthese segments is played during a “segment slot” 8806, which is a periodof time which is to be filled with one of the video segments 8802 fromthe segment bank 8804. In the example depicted in FIG. 88, there arethree segment slots 8806 (plus a Beginning Segment and an EndingSegment) in the video message 8801. The segment bank 8804 contains sixsegments 8802, Segment A-Segment F. The first segment slot 8806 isfilled with Segment D, the second segment slot 8806 is filled withSegment F, and the third segment slot 8806 is filled with Segment A.Thus, playing the video message 8801 in FIG. 88 would result in playingthe Beginning Segment, followed by Segment D, then Segment F, thenSegment A, and finally the Ending Segment. Since each video segment 8802is different in some way, some segments 8802 will be preferabledepending for certain attendee demographic targets or attendeeinterests. In some embodiments, the system operator chooses whichsegments 8802 will fill each segment slot 8804 of the video message 8801and actually makes the decision of which segments 8802 to use duringeach segment slot 8804 while the video message 8801 is playing. In theseembodiments, system operator uses real-time data being presented whilethe video message 8801 is being played to choose which video segment8802 to use to fill the next segment slot 8806. For example, demographicdata might show that most attendees are women, prompting the systemoperator to choose a female-oriented message segment 8802 for the firstsegment slot 8806. While that segment 8802 is playing, the systemoperator can use real-time measurement data being compiled fromresponses to determine which segment 8802 from segment bank 8804 will bebest to fill the second segment slot 8806. In this manner, the systemoperator can use real-time measurement data to create video segmentsthat are customized to take advantage of the most up-to-date attendeeinformation available.

The process described above and depicted in FIG. 88 uses different videosegments to build a full-length video message. However, system operatorscan use real-time information to dynamically create videos in otherways. In other embodiments, the system operator does not combinesegments to create a video, but changes things about a video message,such as the color of the background of the video message, the musicbeing played with the video message, or length of the video message.

Turning to FIG. 89, there is illustrated a flowchart depicting how, insome embodiments, responses are accumulated and displayed as part of asample window within a query response window. The process starts withstart block 8902 and moves to function block 8904, where the queryresponse time window is configured and set up. Next, the process movesto function block 8906, where the sampling rate for the query timewindow is set. This determines how long each sample window will last.The process then flows to decision block 8908, where a decision is madewhether or not to accept all responses, or only to accept each device'sfirst response during the query response time window. If the system isonly to accept each device's first response, the process flows tofunction block 8910, where a filter is set up to accept only oneresponse from each device during the time window. The process then flowsto function block 8914. If, at decision block 8908, all responses are tobe accepted, the process moves to function block 8914, which sets thefilter to accept all responses from each device. Next, the process movesto block 8914. At function block 8914, the query response window isinitiated. Next, at function block 8916, the accumulation register for asample window starts, which will begin counting the responses. Next, theprocess flows to function block 8918, where the system begins acceptingresponses from attendees. The process flows to decision block 8920,where, if the sample window has not ended, the process loops back tofunction block 8918 to continue accepting responses. If the samplewindow has ended, the process moves to function block 8922, where thesystem stops accumulating responses for that sample window. The processmoves to function block 8924, where the system stores the results of theaccumulated data for the sample window. Here, the results for thejust-ended sample window are stored as an object in an object-orienteddatabase. For example, the object can store information related to suchthings as the sample number, the response selections and numbers, therelated query, and the time of the sample window or the time duration ofthe sample window. Storing the information in an object-orienteddatabase allows the information to be linked to information contained inother objects in the database. The process then moves to function block8926, where results for the sample window data are displayed. Next, theprocess moves to decision block 8928, where, if the query window has notended, the process loops back to function block 8916 to begin anaccumulation register for a new sample window. If, at block 8928, thequery response window has ended, the process goes to block 8930, wherethe process ends.

Turning to FIG. 90, there is illustrated an example of a relationaldatabase correlating queries with response time windows. Table 9000 hastwo columns, column 9002, which lists queries, and column 9004, whichlists time windows. The table shows, for each query 9006 presented tothe event attendees, the beginning and ending times 9008 of the timewindow during which received responses will be associated with thatquery. For example, query Q2 has a response time window of T3-T4. Thismeans that all responses from MUs 110 received after time T3, but beforetime T4, will be associated with query Q2 and will be presumed to beresponses to query Q2. If a response is received outside of the timewidow from T3-T4, it will not be associated with query Q2, even if theuser of the MU 110 that submitted the response actually intended theresponse to be to query Q2. Some embodiments will have only one timewindow per query. Other embodiments will have multiple time windows forat least one of the queries. In some embodiments, the time windows willall be the same length of time. In other embodiments, at least some ofthe time windows will be of different lengths of time.

Turning to FIG. 91, there is illustrated a graph 9100 which demonstratesan example of how UID responses are marked with time stamps andassociated with specific queries. Horizontal axis 9102 indicates timeduring a venue event, with time increasing from the left end of the axisto the right end of the axis. Vertical axis 9104 indicates which UID amobile unit's 110 response is associated with. Graph markers 9106indicate a response from a mobile unit 110. A graph marker's 9106horizontal location indicates the time at which the response wassubmitted, while the graph marker's vertical location indicates whichUID submitted the response. Horizontal bars 9108 indicate the timeduring which the time window for a specific query is open. Each bar islabeled (Q1, Q2, Q3, or Q4) to indicate which query the time window isfor. The time markers 9110 are times at which time windows for queryresponses begin and end. In the example shown in FIG. 91, the timewindow to respond to query Q1 starts at time T1 and ends at time T2.Since all responses are time-stamped, any responses received from MUs110 during this time will indicate they were submitted between time T1and time T2. All four UIDs—UID(1), UID(2), UID(3), and UID(4)—eachsubmitted one response during the time window beginning with T1 andending with T2. This means that each UID will have one responseassociated with query Q1. Note that UID(3) also submitted a response9106 a during the time between T2 and T3. Since the time between T2 andT3 does not fall into the time window for any query responses, thatresponse submitted by UID(3) will not be associated with any query. TwoUIDs, UID(1) and UID(2), submitted responses (9106 b and 9106 c,respectively) between times T3 and T4, which is the time window forresponses to query Q2, meaning that these responses will be associatedwith query Q2. Since no responses from UID(3) or UID(4) were submittedbetween times T3 and T4, no responses from UID(3) or UID(4) will beassociated with query Q2. For query Q3, three responses were submitted:9106 d by UID(2), and 9106 e and 9106 f by UID(3). Response 9106 d willbe logged as a response from UID(2) to query Q3. However, because bothresponses 9106 e and 9106 f were submitted by the same UID, UID(4),during the same time window (the time window for Q3), the situation iscreated where both responses 9106 e and 9106 f will be associated withquery Q4. Different embodiments will treat this situation in differentways. In some embodiments, the first response received from a UID duringa time window for a query response will be the only response from thatUID ultimately correlated with that particular query. In otherembodiments, the last response received will be the only responsecorrelated with the query. In other embodiments, if more than oneresponse is received from a UID during a single time window, more thanone of the responses will ultimately stay associated with the query forthat time window and analyzed for statistical information. The specificnumber of queries, responses, and UIDs in FIG. 91 are only examples, asare the time window lengths.

In some embodiments of the disclosure, there are times during the liveevent that do not fall within the time window for any query response. Inother embodiments, any time between the start of the first time windowand the end of the last time window falls within one time window oranother. In some embodiments, only one response per UID per time windowis analyzed as part of statistical information gathering. In otherembodiments, multiple responses per UID per time window are analyzed aspart of the statistical information gathering.

Turning to FIG. 92, there is illustrated a diagram of a table 9202,which represents an example of a relational database relating queriesand query responses to the meanings of query responses. In the firstcolumn of table 9200, there are listed the queries 9202 that are to bepresented to venue attendees. In the second through fifth columns, thereare listed in the first row the different responses 9204 that can besubmitted by the mobile units 110 to the queries 9202. In this example,the responses can be “A”, “B,” “C,” or “D.” Finally, in the columnsunder each response possibility are listed meanings 9206 of eachresponse when it is associated with a particular query. For example, themeaning of a response of “C” to query Q3 is stored in R3C. Continuingwith this example, if query Q3 states, “What is your favorite pizzatopping? A) Pepperoni, B) Cheese, C) Anchovies, or D) Mushrooms,” thenR3C would have a meaning of “Anchovies.” Using this information, it canbe determined that a response of “C” to Q3 indicates that the userselected “Anchovies” as the response to the query “What is your favoritepizza topping?” The embodiment shown in FIG. 92 is simply an example.Some embodiments will have more than four possible responses for eachquery. Some will have fewer than four. In some embodiments, not allqueries will have the same number of response possibilities. In someembodiments, the response choices will be alphanumeric characters. Inother embodiments, the choices will be different shapes. In otherembodiments, the choices will be icons of various colors. Someembodiments will have response meanings that are combinations ofresponse choices.

In some embodiments, not only are responses to queries collected duringthe event, but statistical information relating to the responses is alsocompiled during the event. In some of these embodiments, thisstatistical information can be displayed to the event attendees. In someembodiments, the statistical information regarding responses isautomatically presented back to the event audience, while in otherembodiments, the decision to present this information to the eventaudience is made by an individual or computer program after theinformation is gathered and analyzed.

In some embodiments of the disclosure, the information correlatingqueries with response time windows is stored in the same database as theinformation correlating query responses with the meanings of theresponses. In other embodiments, the time correlation information isstored in a database that is different than the database storing theresponse meaning information. In some embodiments, all of the relationaldatabase information is stored locally at the event location, such thatno information from an external internet is required to relate queryresponses to response time windows and response meanings.

In some embodiments, responses from mobile units include informationrelated to the event venue from which the responding UID is located. Insome embodiments of these, this information utilized a relationaldatabase which correlates the location information with an event venue.

Turning to FIG. 93, there is illustrated a diagram of a set 9302 ofseveral media messages 9304. In this example, each message in the set isa commercial for a different kind of automobile, with each message beingone that can be presented after the query has been presented to theaudience and the query responses have been analyzed. Message 9304 a (M1)is a commercial for a sports car, message 9304 b (M2) is a commercialfor a luxury sedan, and message 9304 c (M3) is a commercial for aminivan. Which of these messages will be presented to the audience isdetermined by the process described hereinabove. The messages in thisset are not simply random commercials that have been lumped together,however. The messages are linked to each other by a common relationshipto a query 9306. Their relationship to query 9306 is such that thestatistical information gathered from responses to query 9306 will helpdetermine which one of M1, M2, or M3 will be presented to attendees.Each of these commercials is produced by the media provider with theintent of being part of a set of commercials for different kinds ofautomobiles, with each commercial to be presented when certainconditions are met. This new production standard, where each of themessages in the set is pre-produced with the specific purpose of beingpart of a set of messages from which a specific message will bepresented, enables media providers to make the best use of theirresources by efficiently producing a set of messages with multipledemographics or other groupings in mind.

Staying with the example in FIG. 93, the automobile manufacturer hasthree demographics that it wishes to reach: single men, married men, andretired men. Keeping this in mind, the automobile manufacturer producesthe three media messages—M1, M2, and M3—with each message beingpurposely produced to appeal to one of the demographics in mind. Thus,no matter which demographic—single men, married men, or retired me—turnsout to be most highly represented when the automobile manufacturer has achance to present one of its commercials, it will have a commercialready to present to the audience that appeals to the most desirabledemographic target. One of the important aspects of these embodiments isthat messages are not produced in isolation, but are produced with theintent of being part of a set or sets. In some embodiments, additionalmessages will be produced and added to a set after an initial set ofmessages is produced, but an important aspect of these embodiments isthat the messages are produced with the purpose of being “linked” to aquery, demographic, etc. In this example, the manufacturer has threedemographics it is targeting, and thus creates a commercial targetingeach demographic.

In some embodiments, the message set is not related to a specific queryor set of responses, but is related to a set of demographic criteria.For example, in a slightly different embodiment, the set of messagesillustrated in FIG. 93, rather than being linked to a specific query, islinked to a set of possible demographic make-ups of the male attendees.Even if the automobile manufacturer does not know how it will determinethe demographic make-up of male attendees, it knows that differentgroups of males respond differently to different types of messages andtherefor creates different commercials directed at each group. In anembodiment such as this, a media provider produces a set of mediamessages for a set of circumstances related to information regarding acertain demographic classification or set of interests. In theseembodiments, the sets of messages are created without regard to a querythat might later be associated with them, or without regard how thestatistical information used to determine which message to be presentedwill be acquired. Rather, the messages are produced knowing as little aswhich different groups of attendees the message provider wishes toreach.

Turning to FIG. 94, this concept is illustrated in another example.Shown in FIG. 94 are several sets 9402 of messages 9404. Each set 9402is of messages 9404 which are each tailored to a particular group ofpotential viewers with a demographic or interest category. For example,set 9402 a is a set of messages that are tailored to viewers ofdifferent genders, with M1A being a message 9404 or advertisementtargeting males, and M2A being a message 9404 targeting females. Set9402 b is of messages which are tailored to attendees of different ages,while set 9402 c is a set of messages each tailored to a different hobbyinterest. Each set of messages is produced together, as a set, by themedia provider. The media provider may not even know whether or not anyone particular message will be presented, but the messages have beenproduced as a set, so that when, and if, the conditions occur for aparticular message to be especially desirable, it is already availableand ready to present.

In some embodiments, in which a message set is related to a query, thequery is produced before the message set is produced. In otherembodiments, sets of messages are produced before specific queries areproduced for the message sets.

Turning to FIG. 95A, there is illustrated an example of multiple sets9502 of media messages from a media provider. Each media message is amessage that can be presented to event attendees. The messages aredivided into different sets, each set being messages that are designedto appeal to a certain demographic. In this example, there are four setsof messages. Set 9502 a is a set of messages designed to appeal tofemale audience members. Set 9502 b is a set of messages designed toappeal to male audience members. Set 9502 c is designed to appeal toyounger viewers, and Set 9502 d is designed to appeal to older viewers.For example, if the media provider providing these sets of messages isan automobile manufacturer, each set of messages could be different setsof advertisements featuring different types of vehicles produced by thatmanufacturer, with each vehicle being promoted to a particular gender orage group. In some embodiments, the subjects of the media messages willbe same for each set, with the individual messages in each set tailoredto a specific demographic. In other embodiments, different message setswill feature different products or services altogether, with theproducts or services being ones that are typically marketed to membersof that set's target demographic.

In some embodiments, media message sets 9504 contain messages that aretailored to multiple target demographics. The benefit of these types ofmessages is that when more information regarding the event attendeesthat are actively responding with their MUs 110 is available, moretargeted messages can be presented and with greater effect. Turning toFIG. 95B, there are illustrated examples of several sets of mediamessages, each with a target demographic, similar to those in FIG. 95A.In this example, sets 9504 a are sets of messages designed to appeal tomale audience members. Sets 9504 b are sets of messages designed toappeal to female audience members. Set 9504 c is designed to appeal toyounger viewers, and Set 9504 d is designed to appeal to older viewers.Unlike the message sets 9502 in FIG. 95A, the message sets 9504 of FIG.95B overlap with other message sets 9504. These overlapping regions 9506of the sets indicate that some of the messages in each set are the samemessages as those in the overlapping set and are designed to appeal toboth overlapping set demographics. For example, region 9506 a is formedby the overlapping regions of the male-oriented set 9504 a and theyouth-oriented set 9504 c. This means that some of the messages in eachof these sets are designed to appeal to both male and young eventattendees. Similarly, region 9506 b indicates that at least some of themedia messages are designed to appeal to older female audience members.Staying with this example, if query response information indicates thata significant portion of responding attendees are young males, then thesystem operator or the operating computer program can select a messageto present which appeals to both males and younger viewers. This allowsthe media provider to get the most benefit out of each of their messagesby providing messages which target multiple demographics at the sametime.

The demographics shown in FIGS. 95A and 95B are only examples. In someembodiments, messages are tailored to gender, while in others, themessages are tailored for age. In other embodiments, the messages aretailored to ethnicity. In other embodiments, the messages are tailoredto income level. In other embodiments, messages are tailored togeographic location of the viewers' residences. Some embodiments willhave messages that are tailored to only a single demographicclassification, while other embodiments will have messages tailored formultiple demographic classifications. In some embodiments, rather thanhaving sets of messages tailored to specific demographics, the sets willbe of messages tailored to specific consumer interests or politicalproclivities.

Turning to FIG. 96, there are illustrated diagrams showing how mediamessages are selected based on information from query responses.Illustrated are representations of media messages, such asadvertisements. Starting with FIG. 96A, there is illustrated arepresentation of a query 9602 which is presented to the eventattendees. The attendees respond with responses, the statisticalanalysis of which makes up response data 9604. In this example, a mediaprovider has supplied three messages 9606 which could be presented toattendees. Each one of these messages is tailored to appeal to adifferent demographic. Which message is presented will depend on theanalysis of response data 9604. For example, if message 9606 M1A isdesigned to appeal to males, M1B to females, and M1C to high incomeviewers, then an analysis of response data 9604 which shows a highproportion of wealthy individuals are responding would result in messageM1C being presented. Each message could be a variation of the sameoverall message, or each message could be totally different from theother two messages. In some embodiments, additional queries andadditional messages will be presented after one of the messages 9606. Inthis manner, information related to the interests and opinions of eventattendees is refined multiple times so that more precisely targetedmessages can be delivered.

A variation of the embodiment is shown in FIG. 96B. This embodiment issimilar to the embodiment shown in FIG. 96A, except that in FIG. 96B, amessage is presented to the event attendees prior to the query beingpresented. In this embodiment, a message 9608 is presented, followed bya query 9610. In some embodiments, the query Q1 is related to themessage M1. After query Q1 is presented, the event attendees respondthrough their MUs 110 with responses which make up response data 9612,or R1. Like in the embodiment shown in FIG. 96A, which message 9614 fromthe group of M2A, M2B, and M2C is next presented depends on the analysisof R1. Each message could be tailored to a specific demographic, or eachmessage could be designed to appeal to viewers with particularinterests. For example, if message 9608 M1 is a commercial for a pickuptruck, then query Q1 could be a question about whether or not thatproduct appeals to attendees. If M2A is a commercial for an auto partswebsite, and M2B and M2C are advertisements for a cooking-interesttelevision channel and a brand of office furniture respectively, then ananalysis of R1, response data 9612, showing that the truck commercialrated favorably with a large portion of responding attendees wouldresult in M2A, the commercial for the auto parts website, beingpresented. The benefit of an embodiment such as illustrated in FIG. 96Bis that queries can be designed to determine how audiences react to apreviously-presented query.

In some embodiments, such as is shown in FIG. 96C, multiple queries 9616are presented and multiple response sets 9618 are collected. In theseembodiments, the multiple response sets are combined and analyzedtogether (represented by block 9620) to determine demographic orinterest information about the responding users. Based on the analysisof the combined response sets 9616, the best message from a set ofmessages 9622 provided by a media provider is presented to the audience.The selection of which message 9622 will be shown is the result of theanalysis of all of the response sets from the queries 9616. In this way,information from multiple queries about event attendees' opinions andinterests is acquired and factored into the decision of which message topresent. One benefit of these embodiments is that since multiple queriesare presented and multiple response sets received before the mediamessage is presented, the message 9622 that is presented can be moreeffectively targeted to the particular attendees currently in the venuethan a message targeted based on only one query and response set.

In some embodiments, the process of presenting queries and thenselecting media messages to present based on responses to those queriesdoes not occur within event venues. In some of these embodiments, themedia messages are presented during television programs, radio programs,websites, or on internet-based webcasts. In some embodiments, thequeries are presented via the same format as the media messages, whilein other embodiments, the queries are presented in different formats, orvia formats such as websites, text messages, “blog” sites (such asFacebook, Twitter, or Snapchat), through mobile device apps, or through“smart devices” such as internet-connected TVs or set-top boxes. In someembodiments, the same kinds of platforms (text messages, websites,microblogs, etc.) are also used for respondents to submit responses. Theformat of the query does not have to be the same format as the queryresponse, however. Virtually any format that allows for a unique ID tobe associated with a user or device serves as an appropriate format forquery responses.

Different embodiments will use different mechanisms to actually make theselection of media message to present next. In some embodiments, theselection is made automatically by a computer within the event venuerunning a program with sets of algorithms which can analyze queryresponse data, possibly in combination with demographic or otheridentifying information from responding users. The computer then choosesthe next message to present to the attendees. In other embodiments, ahuman system operator makes the selection. In some of these embodiments,the system operator makes the selection based on a predetermined set ofconditions related to query response data, while in some embodiments,the system operator makes the decision at least partly based on his orher own judgement as to which message would be the best to present.

Turning to FIG. 97, there is illustrated an example of some embodimentsin which statistical information is used to determine the most desiredproduct features and to create media based on these features. In FIG.97, there are illustrated representations of a number of presentations9710. Each presentation 9710 presents a product or service with a set offeatures or attributes related to that product or service. Thepresentations 9710 are arranged in a hierarchical format such that eachpresentation 9710 is at a certain hierarchical level 9712 and branchesoff from a presentation one level 9712 above. For example, presentation9710 labeled P1 is at the top level 9712 (“LVL 1”) of the hierarchy andbranches off to presentations labeled P2A and P2B, which are both onelevel 9712 (“LVL 2”) below level 9712 LVL 1. All of the product featuresincluded in a presentation 9710 are also included in the presentationone level 9712 below, however, each presentation 9710 will have at leastone feature changed or not included in the presentation with which it isassociated one level 9712 higher. Thus, both presentations 9710 in LVL 2have the features of presentation P1, as well as an additional featurefor each. The products or services shown in presentations 9710 labeledP3C and P3D are one level 9712 below LVL 2 will have all of the featuresof the product shown in the presentation P2B. However, the product orservice presented in P3C will have an additional feature or features notincluded in the product of presentation P2B. Similarly, the product orservice shown in P3D will have all of the features of the product shownin P2B, as well as at least one additional feature. As an example, ifpresentation P1 shows a new car, presentation P2A could show the samecar with the added feature of a sunroof, while P2B would show the samecar as in P1, but with the added feature of a premium stereo system.Thus, a presentation 9710 at the bottom level 9712 will have all of thefeatures of the related presentations 9710 in all of the higher levels9712. Presentation P4G, for example, will have a product or service withall of the features of the products or services in presentations 9710P1, P2B, and P3D, as well as an additional feature not included in thehigher levels 9712.

In some embodiments, the features added to the lower levels 9712 will bevariations of the same feature. For example, if P1 is a presentationshowing an advertisement for a credit card, P2A and P2B could each showdifferent kinds of benefits enjoyed by card members, such as “cash back”in P2A and airline miles in P2B. In some embodiments, the product orservice shown in a presentation 9710 will not have an additional featureas compared to the product in the associated presentation 9710 one level9712 higher, but will instead hold that feature constant, while aproduct in a presentation 9710 at the same level 9712 and alsoassociated with the same presentation 9710 one level 9712 higher willhave a variation of that product. For example, the presentation P3Bmight show a red product. P4C and P4D, both branching off of P3B, wouldshow for the same product, except that while P4C would show the same redproduct, P4D would show a green version of the product. This allowsvariations in features which are simply intrinsic to the nature of theproduct or service being presented. The types of products, productfeatures, services, and service features described in these embodimentsare only examples. In some embodiments, the added feature shown in apresentation will actually be lack of or the removal of something thatexisted in the associated presentation one level higher.

In some embodiments, the feature that is changed in the presentation9710 is not a feature of the product or service, but is instead afeature or aspect of the presentation 9710 itself, such as the choice ofmusic, or the time length of the message. For example, in someembodiments, the feature added to the presentations 9710 a level 9712below a presentation will be a voiceover narration. In anotherembodiment, the feature varied will be the weather present in a scenedepicted in the presentation 9710. In another embodiment, the featurechanged will be the addition of a celebrity actor or actress.

By varying features of products and services in presentations or of thepresentations themselves, the best combinations of features forpresentations and products can be determined. This, in turn, will allowfor the most popular and in-demand presentations and products to beproduced.

Turning now to FIG. 98, there are illustrated representations of severalpresentations 9810 arranged in a hierarchical format in different levels9820, such as were illustrated in FIG. 97. In practice, to determinewhat features (of either products or presentations) are most appealingto viewers, all of the presentations with features being compared wouldbe presented to audiences (not necessarily the same audience). After apresentation 9810 is shown to an audience, the positive reaction to thepresentation is measured via some type of query and set of responses.The positive reactions of presentations 9810 branching from the samepresentation one level 9820 higher are then compared, and thepresentation with the more positive reaction is selected as the “path”of the hierarchy on which to advance, with the feature (either or theproduct or of the presentation itself) integrated into presentationswhich branch from it in every level 9820 further down the hierarchy. Inother words, every presentation that branches from a “parent”presentation will have all of the features included in the “parent”presentation, along with a feature added at each additional hierarchicallevel 9820. In this way, the product or presentation becomes more andmore refined and tuned to what elicits a favorable reaction from viewersof the presentation.

Turning again to FIG. 98, an example embodiment is given. Thepresentation 9810 labeled P1 is a presentation for brand of automobileand is at the first hierarchical level 9820, labeled LVL 1. P1 ispresented to an audience, and a query is used to determine the positivereaction to P1. In this example, when shown to an audience of eventattendees, P1 elicits a positive response from 23% of the attendees. Thefeature to be added at the next level 9820, LVL 2, is adding a specifictype of car (from the brand shown in P1) to the presentation 9820.Moving one level 9820 down from P1 to LVL 2, P2A and P2B arepresentations for a mini-van and a sports car, respectively. In thisexample, P2A receives a 25% positive response from an audience query(25% of all of the respondents responded positively), while P2B receivesa 35% positive response. Since the sports car of P2B received the higherpositive response than the mini-van of P2A, the subsequent presentationsshown, starting with those from the next level 9820 (LVL 3), will befrom P2B's “branch” of the hierarchy.

The next presentations shown, at LVL 3, will vary the color of thesports car shown in P2B. P3C shows a blue sports car, while P3D shows agreen sports car. In this example, P2B also showed a sports car that wasgreen. As explained above herein, this is consistent with otherembodiments, as color is an intrinsic feature of automobiles, andkeeping constant an intrinsic feature for a level 9820 of presentations9810 still gives an indication of what version of the feature (in thiscase, color) the event attendees prefer. After P2A and P2B are shown,the next presentations from the preferred branch (in this case, P2B'sbranch) are shown. In this example, P3C, the blue sports car, garnered a43% positive reaction from queries after being shown to an audience,while P3D, the green sports car, again garnered a 35% positive reaction.The blue sports car of P3C being favored over the green sports car ofP4D, the presentation hierarchy path moves to the next level 9820 downfrom P3C to P4E and P4F, each of which adds a luxury feature to the bluesports car of P3C. In this example, P4E adds a premium stereo system tothe blue sports car, while P4F adds a sunroof. After both presentations9810 are shown to the event attendees, P4E receives a 74% positiveresponse from a query, while P4F receives a 52% positive response,meaning that the audience prefers a stereo system to a sunroof. Withthis information, the generators of the media messages learn that, forthe automobile manufacturer shown in presentation P1, the vehicle mostfavored by consumers is likely a blue sports car with a premium soundsystem.

By following the process described herein above and illustrated in FIG.97, with an example of an embodiment described in FIG. 98, producers ofmedia messages will be able to determine what features of products,services, and presentations receive the most favorable responses and arethus most appealing to consumers. Once media producers have thisinformation regarding consumer preferences, they can begin to create newmedia presentations or media campaigns which include or place emphasison the features most desired by consumers.

Turning now to FIG. 99, there is illustrated a flowchart detailing theprocess of producing media based on the most desired product features.The process starts with Start block 9902 and moves to function block9904, where a presentation with a product or service is shown to eventattendees. The process then moves to block 9906, where the attendees'positive response to the presentation is measured. This can be measuredvia responses to queries or by the share of attendees that respond atall. The process flows to decision block 9908. If another presentationto compare with the presentation shown in block 9904 is to be shown, theprocess moves to block 9910, where a feature of the presentation, or ofthe product/service in the presentation, is varied to create anotherpresentation. The process then loops back to block 9904. If, in block9908, another presentation will not be shown, the process moves todecision block 9912. Here, if there are responses to multiplepresentations to compare, the process moves to function block 9914,where the responses of the various presentations at that hierarchy levelare compared to determine the most appealing feature. The process movesto block 9916, where the most appealing feature of the set compared inblock 9914 is implemented into future presentations. The process movesto block 9918, where it is determined whether or not there will beanother hierarchical level of features to compare. If not, the processends at block 9922. If so, the process flows to block 9920, where thesystem moves to the next hierarchical level of product or presentationfeatures to vary and compare. The process flows to block 9910, where afeature is varied to create a new presentation, and again loops back toblock 9904 to show the new presentation.

Turning to FIG. 100, there is illustrated the same example shown in FIG.98 depicted in a different way. In FIG. 100, there are severalhierarchical levels 10010 populated with presentations 10020, thepresentations 10020 in each level 10010 having the features of thepresentations 10020 in higher hierarchical levels 10010 from which theybranched (as shown in FIG. 31). Again, the process starts at the highesthierarchical level 10010, LVL 1, by showing presentation P1, apresentation 10020 for a brand of automobile. Using queries, it isdetermined that 23% of responding attendees have a positive reaction toP1.

Next, sometime after P1 has been shown and reaction data from theaudience collected, the system moves one hierarchical level 10010 down,to LVL 2, where two more presentations 10020, P2A and P2B, are shown,wherein P2A is an advertisement for a minivan produced by the automobilebrand shown in P1, and P2B is an advertisement for a sports car producedby the automobile brand shown in P1. Queries are used to determine thepositive responses of the event attendees to P2A and P2B, and, in thisexample, P2A has a 25% positive response, while P2B has a 35% positiveresponse. Since P2B had a higher positive response than P2A, the pathdown to the next hierarchical level 10010 will go through P2B, meaningthat the next presentations 10020 to be shown will have the features ofthe sports car from P2B made by the automobile manufacturer from P1.

Sometime after P2A and P2B are shown, and audience responses show thatP2B is the more preferable of the two, two more presentations 10020, P3Cand P3D, from the next hierarchical level 10010, LVL 3, are shown. P3Cand P3D branched off of P2B in the hierarchy, so they both show sportscars made by the same manufacturer as in P1. However, the sports car inP3C shows a blue sports car, while P3D shows a green sports car.Responses to each of these presentations are measured, showing a 43%positive reaction to the blue sports car in P3C and a 35% positivereaction to the green sports car. Since P3C had a higher positivereaction than P3D, it is determined that an advertisement with a bluesports car is preferable to an advertisement with a green car, so thepath to the next hierarchical level 10010 down, LVL 4, will branch offof P3C and will feature blue sports cars.

Sometime after the determination that P3C is preferable to P3D, two morepresentations 10020, P4E and P4D, will be shown. Both of thesepresentations are one level 10010 down from P3D in LVL 4 and branch offof P3D. Thus, they will both feature blue sports cars from themanufacturer featured in the very first presentation 10020, P1. In thisexample, the added feature at LVL 4 is the addition of a luxury featureto the automobile. Presentation 10020 P4E adds a premium sound system,and P4F adds a sunroof. Thus, P4E is an advertisement for a blue sportscar with a premium sound system, and P4F is an advertisement for a bluesports car with a sunroof.

Sometime after the presentations 10020 of hierarchical level 10010 LVL 3are shown and data gathered for, P4E and P4F are shown to an audience.In this example, P4E garners a 74% positive response, and P4F garners a52% positive response, so it is determined that a premium sound systemis more preferable to consumers than is a sunroof. Thus, P4E, theadvertisement for a blue sports car with a premium sound system, ischosen for the path down which to travel to the next hierarchical level10010, if one exists. In this example, however, LVL 4 is the lowesthierarchical level 10010. Therefore, the presentation 10020, P_(FINAL),showing the most desired product feature is a combination of thefeatures of presentations 10020 P1, P2B, P3C, and P4E, meaning that themost desired product feature from that automobile manufacturer is asports car (from P2B) that is blue (from P3C) and has a premium soundsystem (from P4E). The automobile manufacturer can now create amarketing campaign focusing on such a vehicle, knowing that consumershave already shown that they are likely to respond favorably.

As discussed hereinabove, it should be noted that in some embodiments,all of the presentations to be shown will be shown at a single eventvenue to the same audience. In other embodiments, however, at least someof the presentations will be shown at a different event in the samevenue, or even at different events in different venues. Thus,determining which of two presentations has a higher positive ratingmight involve showing the presentations to multiple audiences andcompiling the results from each to obtain a final set of data which cancompare the reactions to the two presentations. The same informationregarding consumer preferences can be collected whether or not thepresentations are shown to the same audiences or at the same venues. Inthese embodiments, information from multiple audiences is compiledtogether. For example, if multiple audiences are given the choice ofchoosing between P3C and P3D from FIG. 31, then the results from eachaudience are compiled together to get a more accurate gauge of whataudience preferences really are with regards to P3C and P3D. Thus,determining which of a pair of presentations garners the higher positivereaction from consumers could take days, or even weeks, if thepresentations are shown to several different audiences over a period oftime, and completing the whole process of determining the most desiredfeature of a series of presentations could potentially take even longer,depending on how many audiences the presentations are shown to and howmuch information is needed to obtain an accurate measurement of whatfeatures consumers prefer.

Turning to FIG. 101, there is illustrated a diagram of how, in someembodiments, rather than using percentages, “weights” are assigned topresentations, whereby when two presentations are compared, thepresentation preferred by the audience is assigned a weight based on howmuch more preferred it was compared to the other presentation. Assigningweights is especially useful for embodiments wherein the entire processworking through the presentation hierarchy occurs multiple times. Ratherthan simply assigning a binary answer to the question of which of twopresentations was more preferred, assigning weights allows thedetermination of which product feature is most desired even whendifferent venues or audiences choose different presentations as mostpreferable. For example, if two presentations 10110, Presentation Q andPresentation X, are being compared, they might be compared by multipleaudiences 10120 (Audience 1 and Audience 2). In this example, eachaudience 10120 chooses which of the two presentations 10110 they prefermost. The first audience 10120, Audience 1, prefers Presentation Q,while the second audience 10120, Audience 2, prefers Presentation X. Ifthe only knowledge available is which presentation was selected aspreferred by the most audiences, then the answer would be a tie, and anaccurate determination could not be made. In this example, however, thepreferred presentation for each audience is assigned a weight based onhow much more preferred it was than the other presentation. For Audience1, Presentation Q was preferred over Presentation X by a 3-to-1 margin(75% to 25%). Thus, by dividing the percentage of attendees who selectedQ by the percentage of attendees who selected X, Presentation Q is givena weight value of 3. For Audience 2, Presentation X was preferred overPresentation Q, but only by a 51-to-49 margin (51% to 49%). It is givena weight of 1.04 (51±49). Therefore, even though each Presentation waspreferred by the same number of audiences 10120 (1 each), Presentation Qhas a much higher weight than Presentation X, and Presentation Q wouldbe chosen as the most preferred presentation as to between PresentationsQ and X.

Turning to FIG. 102, there is illustrated a diagram of how, insituations wherein multiple audiences are used to compare presentations,a final weight value 10240 is given to each presentation which is thesum of the weights 10230 given to it by all of the audiences queried.For example, in FIG. 102, three audiences 10220 are used to compare twopresentations 10210, Presentations Y and Z, with audience membersresponding to queries to select which presentation 10210 they prefer.Audience 1 prefers Presentation Y over Presentation Z 60%-to-40%, givingPresentation Y a weight of 1.5. Audience 2 prefers Presentation Z overPresentation Y 55%-to-45%, giving Presentation Y a weight of 1.22.Finally, Audience 3 prefers Presentation Y over Presentation Z65%-to-35%, giving Presentation Y a weight of 1.86. Summing the weightseach presentation 10210 garnered from each audience 10220 means thatPresentation Y has a final weight value 10240 of 3.36 (1.5+1.86), whilePresentation Z has a weight of 1.22. Since Presentation Y has the higherfinal weight value 10240, it will be selected as the more preferred ofthe two presentations 10220. This process can be expanded to as manydifferent venues and audiences as necessary.

In some embodiments, all responses are factored into determining themost desired product feature. In other embodiments, however, systemoperators might only care about a certain segment demographic ofattendee, such as a particular gender, income level, age, or any othercategory by which attendees can be divided. In these cases, only thedemographic or demographics that the system operator is targetingfactors into deciding which presentation is most preferable. Forexample, a pickup truck manufacturer might want to create a new adcampaign targeting males between the ages of 25 and 49. When queryingaudience members about presentations, the truck manufacturer would onlyfactor in responses from respondents likely to be males between 25 and49 into determining which presentations are most preferable. Even ifaudience members not fitting into that demographic respond to queries,their input will not factor into the calculations, or will not factor asheavily as will the targeted demographic. As another example, if asporting goods manufacturer is planning to create an ad campaign forbaseball bats, then it might only consider the responses of audiencemembers who responded in a previous query that baseball is theirfavorite sport.

Referring now to FIG. 103, there is illustrated a flow diagram of oneembodiment of a spontaneous event polling process 10300. The process10300 begins at decision block 10302, where it is determined whetherthere currently is downtime at a venue. Downtime can be any lull inactivity at the venue. For example, downtime may occur between plays ata sports event, between bands at a concert, or during intermission at aplay. If there is not currently downtime at the venue, decision block10302 repeats. If there is currently downtime at the venue, the process10300 moves to step 10304 where a spontaneous query is chosen to pollthe audience. It will be understood that such a query may be a question,game, video, or any other type of content displayed on a venue screen,as disclosed herein. The spontaneous query is a query that is chosensoon before the query is asked. It is not a query previously chosen tobe asked at the time of the specific downtime, but, rather, wouldtypically be chosen when there is unexpected downtime at a venue and thevenue wants to keep the audience entertained or engaged. The query maybe a pre-made query, chosen from a list of available queries to be usedfor spontaneous polls, or it may be written during or shortly before thedowntime. In some embodiments, an individual operating a control boothat a venue may choose or write the query, or a query may be sent to thecontrol booth remotely. In other embodiments, the query generation maybe automated by the control booth.

Once a query is chosen, the process 10300 moves to step 10306, where thequery is submitted to a display screen at the venue, in accordance withthe methods described herein. At step 10308, answers from attendees whohave answered the query are received at the local server.

Referring now to FIG. 104, there is illustrated a flow diagram ofanother embodiment of a spontaneous event polling process 10400. Theprocess 10400 begins at decision block 10402, where it is determinedwhether a newsworthy event has occurred. This newsworthy event may beany number of occurrences, such as a presidential candidate beingelected, a crime being committed, an act of war, or a movie winning abest picture award. If a newsworthy event has not occurred, the decisionblock 10402 repeats. If a newsworthy event has occurred, the process10400 moves to step 10404, where a spontaneous query is chosen to pollthe audience. It will be understood that such a query may be a question,game, video, or any other type of content displayed on a venue screen,as disclosed herein. The spontaneous query is a query that is chosensoon before the query is asked. It is not a query previously chosen tobe asked at the time of the specific downtime, but, rather, wouldtypically be chosen when there is unexpected newsworthy event. The querymay be a pre-made query, chosen from a list of available queries to beused for spontaneous polls, or it may be written during or shortlybefore the downtime. In some embodiments, an individual operating acontrol booth at a venue may choose or write the query, or a query maybe sent to the control booth remotely. In other embodiments, the querygeneration may be automated by the control booth.

At decision block 10406, it is determined whether there currently isdowntime at a venue. Downtime can be any lull in activity at the venue.For example, downtime may occur between plays at a sports event, betweenbands at a concert, or during intermission at a play. Downtime may alsobe created by the newsworthy event. If the newsworthy event is of greatimportance, the activity at the venue may be halted, creating downtime.If there is not currently downtime at the venue, decision block 10406repeats. If there is currently downtime, the process 10400 moves to step10408, where the query is submitted to a display screen at the venue, inaccordance with the methods described herein. At step 10410, answersfrom attendees who have answered the query are received at the localserver.

In some embodiments, the venue at which the collection of statisticaldata occurs is a NASCAR event. NASCAR, the National Association forStock Car Auto Racing, is motorsports' preeminent stock-car racingorganization. NASCAR sanctions over 1,500 races at over 100 tracks in 39U.S. states and in Canada. NASCAR is also the second highest ratedprofessional sports franchise in terms of television viewership. Becauseof the popularity of NASCAR races, NASCAR viewers and attendees arehighly coveted as targets of advertisements, political messages, andother types of media messages. In addition, the price of tickets toNASCAR events makes it such that NASCAR attendees are typically moreaffluent, and therefore more desirable message targets.

NASCAR events also attract fans who tend to have more varied intereststhan the general population. In particular, NASCAR fans tend to bepeople who are interested in or “in-tune with” automotive technology andproducts. They also have a higher interest in driving, drivingactivities, and other driving motorsports, such as motorcycle racing,than do members of the general population. Many NASCAR race tracks allowfans to camp on or near the racetrack property. This aspect of NASCARalso attracts fans who are camping enthusiasts and/or recreationalvehicle enthusiasts.

NASCAR races are also “continuous” events. Unlike many other sports,which have significant periods of downtime between innings, halves,quarters, time-outs, etc., NASCAR races have very little, if any,“downtime” from the beginning of the race until the race is completed.The cars are continuously driving around the track without any breaks.Given that a NASCAR race can take upwards of three hours to complete, itis unlikely that fans will be focused exclusively on the race the entiretime, without ever looking at other parts of the venue, or leaving forconcessions, the restroom, or simply to get up and stretch. This givesadvertisers or other messengers plenty of opportunities to use raceevents as part of their messaging to race attendees. Instead of havingto wait for breaks between innings, quarters, etc., advertisers andother messengers can present media messages to the audience at almostany time.

Because of how loud NASCAR vehicles are, many NASCAR fans wear audioheadsets during the races. They use these headsets to listen to audiofeeds from radio stations, or from other sources, such as broadcastswithin the NASCAR racetrack, over a network within the racetrack, oreven from over the internet. This creates another avenue by which fanscan be reached by advertisers or other media messengers. For example,the cues which prompt the mobile users to input a response into the MU110 could be an audio cue delivered through the audio headsets.

NASCAR events typically take place in open-air venues. In other words,NASCAR race tracks are usually relatively open environments with noceilings or roofs above the track or stands where the attendees watchthe race. Because of this, visual cues could even be delivered to eventattendees by blimps, skywriting, airplanes with banners, or evenunmanned aerial vehicles (“drones”) carrying some type of signage suchas a banner.

Advertisers not only desire to deliver their messages to high-incomeaudiences, they often desire information about the individuals in theseaudiences, including their buying habits, political preferences, gendermake-up, ethnic make-up, ages, hobbies and even other sports interests.This means that such statistical information gathered from a relativelyhigh-income audiences like those at NASCAR events is very valuable.Thus, in some embodiments, the statistical information gathering willtake place at a venue in which a NASCAR event takes place. In general, aparticular venue for a particular event will have associated therewith aparticular set of demographics and attendee profiles, and these will beduplicative between events.

TABLE 18 GENDER DISTRIBUTION OF NASCAR FAN BASE Male 63% Female 37%

TABLE 19 AGE DISTRIBUTION OF NASCAR FAN BASE 18-24 years old  5%-15%25-34 years old 15%-20% 35-44 years old 17%-23% 45-54 years old 20%-25%55-64 years old 15%-20% 65+ years old 13%-17%

TABLE 20 INCOME DISTRIBUTION OF NASCAR FAN BASE Under $30,000 18%-23%$30,000-$50,000 23%-27% $50,000-$75,000 17%-21% $75,000-$100,000 13%-17%$100,000+ 18%-22%

TABLE 21 GEOGRAPHIC DISTRIBUTION OF NASCAR FAN BASE Northeast 13%-17%Northwest 23%-27% South 38%-42% West 18%-22%

TABLE 22 ETHNIC MAKE-UP OF NASCAR FAN BASE White   92-96% Hispanic 7%-11% African-American 1%-3%

TABLE 23 VIEWS OF NASCAR FAN BASE Support gun laws that are more 18%-25%lenient compared to general US population Religious beliefs areimportant 20%-30% Don't mind paying extra for 60%-70% NASCAR productsPurchase specific products because 70%-75% they are loyal to a specificNASCAR driver Will purchase an item because they 50%-55% feel likecollecting memorabilia is important Can name every sponsor on the Top30%-40% 30 cars that are racing in a given year

TABLE 24 ENGAGEMENT OF NASCAR FAN BASE TO THE SPORT Avid fans who willuse Facebook to 60%-65% share information about NASCAR Own a smartphoneor mobile device 25%-30% with a NASCAR app they use regularly Use theinternet on a regular bases to 75%-80% get updates on NASCAR Will shareat least some information 90%-95% about NASCAR on a social networkMillennial fans who would 80%-85% recommend watching NASCAR to theirfriends

TABLE 25 TOPICS OF IMPORTANCE TO NASCAR FAN BASE Feel that buying asponsor's 50%-55% products allows them to contribute to the sport Likerelationships that are created 80%-85% through the system of corporateNASCAR sponsorship States the system of corporate 90%-95% sponsorship isvery important to the existence of the sport Unaided sponsor awarenessof 45%-50% businesses involved in NASCAR

TABLE 26 TYPE OF FAN OF NASCAR FAN BASE Upper Avid 10%-15% Middle Avid 8%-12% Lower Avid 13%-17% Upper Casual 45%-50% Lower Casual 15%-20%

TABLE 27 AGE DISTRIBUTION OF FEMALE NASCAR FAN BASE 18-24 10% 25-34 16%35-44 18% 45-54 21% 55-64 17% 65+ 17%

TABLE 28 EDUCTION OF FEMALE NASCAR FAN BASE Some college or higher 57%

TABLE 29 SOCIAL MEDIA USE OF FEMALE NASCAR FAN BASE Used Facebook inlast month 54% Used YouTube in last month 42% Used Google+ in last month29% Used Pinterest in last month 23% Used Twitter in last month 22% UsedInstagram in last month 14% Used Vine in last month 7%

TABLE 30 TECHNOLOGY USE BY FEMALE NASCAR FAN BASE DVD player 86% HD TV62% Smartphone 61% Digital camera 59% Blu-ray player 31% Tablet 26%Camcorder 37% MP3 player 30% Home theater equipment 17%

TABLE 31 TV CHANNELS WATCHED BY FEMALE NASCAR FAN BASE The WeatherChannel 33% History 32% A&E 29% Food Network 28% HGTV 27% USA Network27% FOX News Channel 27% TNT 26% Lifetime 26% Hallmark Channel 25%

TABLE 32 MAGAZINE READERSHIP BY FEMALE NASCAR FAN BASE (IN PAST 6MONTHS) Better Homes and Gardens 42% People 37% Good Housekeeping 32%Woman's Day 29% Family Circle 26% Reader's Digest 20% Us Weekly 20%

TABLE 33 LEISURE ACTIVITIES OF FEMALE NASCAR FANS (PARTICIPATED) Listento music 74% Reading books 69% Dining out (not fast food) 69% Baking forfun 56% Card games 47% Going to beach/lake 47% Cooking for fun 46% Boardgames 42% Gardening 41% Barbecuing 38%

TABLE 34 LEADING INTERNET ACTIVITIES OF FEMALE NASCAR FAN BASE E-mail61% News/Weather 45% Banking 40% Online games 26% Shopping (gatheringinformation) 20% Shopping (making purchase) 19% Watch TV/movies online14%

TABLE 35 TRAVEL HABITS OF FEMALE NASCAR FAN BASE Traveled domesticallyin past year 69% Traveled internationally in past 3 33% years Took acruise in last 3 years 14% Stayed at a hotel domestically 71% Rented avehicle 30% Traveled by plane domestically 22%

TABLE 36 RETAIL CHANNELS SHOPPED BY FEMALE NASCAR FANBASE Supermarkets98% Drugstores 82% Mass retailers 75% Convenience stores 58% Automotiveretail stores 57% Department stores 57% Strip malls 48% Home improvementstores 38% Wholesale clubs 33% Home furnishing stores 19% Officesupply/computer stores 18% Sporting goods stores 10%

TABLE 37 ATTITUDES OF FEMALE NASCAR FAN BASE TO AUTOMOTIVE TECHNOLOGY “Ilike driving” 63% “I am interested in what goes on 49% under the hood”“I am possessive about my car” 43% “You can tell a lot about someone 30%by the car they drive” “My car should express my 29% personality” “I'dpay extra for an engine with 22% more horsepower” “I keep up on thelatest advances in 16% automotive technology” “Friends and family alwaysask my 8% advice on what car they should buy”

Turning to FIG. 105, there is illustrated an embodiment that addressesthe problem of not taking into account that many attendees arrive earlyfor pre-game or pre-event activities. This embodiment uses the mobiledevice survey systems described hereinabove to begin segmenting eventattendees during pre-game or pre-event activities. In these embodiments,the system and process is used not just during the event whilephysically inside the event venue. Instead, these embodiments beginengaging and segmenting event attendees before the event even starts.For example, before many professional football games at many stadiums,event attendees participate in pre-game activities in the parking lots,or some other space, near the stadiums where they grill food, enjoyadult beverages, listen to the radio, watch TV, play games, or conductother activities typically associated with “tailgating.” Concerts andother events often have pre-event activities, too. In many cases, thepre-event or pre-game activities are sponsored or encouraged by theoperators of the event venue. Often, these pre-event activities startseveral hours before the event actually begins, with a significantportion of the event attendees participating in the pre-eventactivities.

Illustrated in FIG. 105 is an example of a venue, a stadium 10510, aswell as pre-game area 10520, which in this case is the area proximate tothe stadium 10510. Individuals 202 participate in pre-event activitiesin the pre-game area 10520. Sometimes, as in this example, the pre-eventactivities occur in an area outside the physical boundaries of the eventvenue, such as in a nearby parking lot, but in other embodiments, thepre-event activities will take place inside the event venue. Additionalwireless network receivers 116 or beacons 602 are placed in or near thepre-event area 10520 so that the wireless network of the event venueextends to include the pre-event area 10520. This allows event attendeesto register their mobile units 110 to create UIDs while outside thephysical boundaries of the event venue. If the pre-event area is outsidethe normal event area, messages, visual ques, and queries are deliveredto attendees via additional screens or signs 10530 that can be seen byattendees in the pre-event area or areas 10520. The screens 10530 couldeven take the form of airplanes with banners, blimps, skywritingmessages, or unmanned aerial vehicles (“drones”) with banners flyingabove the pre-game area 10520. The “footprint” of the venue, forpurposes of using the system to engage event attendees is the areacreated by the overlap of the area defined by of the range of thewireless network created by the wireless receivers 116 and the areadefined by the range at which visual cues from screens or displays 10530can be seen.

As mentioned hereinabove, not all pre-event or pre-game activities takeplace outside the event venue. In some embodiments, the pre-eventactivities take place in the same area as the actual event, meaning thatthe pre-event area 10520 would be the same area as the event venue wherethe main event takes place.

By implementing the system for pre-game or pre-event activities, moreinformation can be gathered about event attendees and their opinions,interests, and preferences. In addition to the information gathering andbinning processes described hereinabove, attendees can be furthercategorized and characterized based on whether or not they attendedpre-event activities and submitted responses during the pre-event time.Engaging event attendees before the event starts provides a number ofbenefits. First, additional information, beyond that which is obtainedduring the event itself, is obtained from attendees who participate andsubmit responses during the pre-event time frame, resulting in a betteroverall characterization of those attendees and increased accuracy when“binning” UIDs into categories. Also, having a data set derivedspecifically from individuals participating in pre-event activities thatis separate from the data set derived from all attendees of the eventitself allows the system operators, advertisers, and others to betterunderstand how the likes, opinions, habits, etc. of pre-event attendeesdiffer from those of event attendees in general. This can be veryuseful, as individuals who attend pre-event activities are typicallymore devoted to or more engaged with the sport, musical group, activity,etc. related to the event. These individuals are also likely to spendmore money on products and services related to the event. Therefore,having information related to these individuals can be highly coveted bymarketers and other media messengers, as they can tailor products orservices, or advertisements for products or services, in a way thatappeals more to pre-event attendees.

Turning to FIG. 106, there is depicted a graph 10602 which illustrateshow UIDs are segmented into “event” and “pre-event” UIDs. Theimplementation of the system for pre-game or pre-event activities occursin a similar way to that described hereinabove for the event itself. Onedifference, however, is that since it is designed to take into accountattendees who arrive for pre-game activities, attendees beginregistering their mobile units and creating UIDs before the eventstarts. UIDs that are created before the start of the main event aretagged as “pre-event” UIDs. This tag can be created based on thetimestamp associated with the creation of the UID. If the timestampassociated with a UID defines a time prior to the start of the mainevent, the UID is tagged as a pre-event UID. The same UID that is usedduring the pre-event time period is also used during the event timeperiod. If the UID timestamp defines a time subsequent to the start ofthe main event, the UID is tagged as simply an event UID. FIG. 106 showsa graph 10602 which depicts the UID population, or the total number ofregistered UIDs, as a function of time. The horizontal axis 10604measures time, which increases from left to right along the axis 10604,and the vertical axis 10606 measures the UID population. The left end ofthe horizontal time axis 10604 represents the pre-event registrationstart time, before the event start time (marked on the axis 10604). TheUID population begins to rise at the start of pre-event registrationtime, signifying that attendees are arriving and creating UIDs duringthe pre-event activities. All of the UIDs created before the event starttime are grouped together as the pre-event population or pre-event UIDs.The pre-event population level is also marked on graph 10602 ashorizontal line 10608. The line 10608 representing the pre-event UIDpopulation does not rise after the event start time, as the pre-eventUID population can no longer increase after that point. However, it isuseful as an illustration of how the pre-event UID population comparesto the UID population as a whole (marked as line 10610).

Turning to FIG. 107, there is illustrated a graph 10702 which shows thenumber of UIDs responding during a query time window as a function oftime. Horizontal axis 10704 represents time, and goes from the start ofthe query time window at the left end of the axis to the end of thequery time window at the right end of the horizontal axis 10704. Thevertical axis 10706 represents the number of UIDs responding during thetime window. Function line 10708 represents the cumulative number of allUIDs (pre-event UIDs and event UIDs) that have responded during the timewindow up to a given point in time. The function line 10710 representsthe cumulative number of pre-event UIDs that have responded during thequery time window up to a given point in time. Region 10712, which isformed by function line 10710, gives an easily understood representationof the number of pre-event UIDs that have responded during the timewindow as compared to the total number of UIDs that have responded.Creating graphs such as graph 10702 is useful, because they provideinformation about the make-up of the responding UIDs. Knowing that alarge proportion of UIDs responding to a particular query are pre-eventUIDs would give useful information to system operators or marketers.Similarly, knowing that the responding UID population is mostly eventUIDs, or perhaps evenly split between event UIDs and pre-event UIDs,could also provide useful information. For example, knowing that aparticular query resulted in a higher proportion of responding UIDsbeing pre-event UIDs could cause the system operators, marketers, orother media providers to make changes to future messages or products totry to replicate the high engagement to that query of pre-eventattendees.

Turning to FIGS. 108A-108C, there are illustrated three pie charts10810, 10820, 10830. Each of these pie charts represents the responsessubmitted to a query with three possible response inputs—X, Y, and Z—anddepicts how the responses were allocated among the three differentresponse options. Each pie chart 10810, 10820, 10830 gives thisinformation with regard to a different portion of responding UIDs. Chart10810 depicts the responses submitted by all UIDs (both pre-event UIDsand normal or event UIDs). As can be seen from chart 10810,approximately half of the responding UIDs selected input “X,” while theother half of responding UIDs were split approximately evenly betweenoptions “Y” and “Z.” Chart 10820 is similar to chart 10810, except thatchart 10820 only shows the breakdown of responses for event, orordinary, UIDs. The pre-event UID responses are not represented. Lookingat chart 10820, it can be seen that responding event UIDs chose each of“X,” “Y,” and “Z” in approximately equal proportions. Finally, chart10830 depicts the allocation of responses by responding pre-event UIDs.In this example, responding pre-event UIDs mostly selected “X,” with theremaining pre-event UIDs approximately evenly split between “Y” and “Z.”These graphs 10810, 10820, 10830 can be created due to the fact thatUIDs are sortable into UIDs and pre-event UIDs, since pre-event UIDs aretagged as such. Being able to create graphs such as graphs 10810, 10820,10830 provides system operators and marketers valuable information,since they will not only be able to see the reactions of event attendeesto a query, but will also be able to see how pre-event attendees respondand compare those responses to regular attendees and will be able tailorfuture advertisements or products according to the opinions of pre-eventattendees, if they wish. For instance, using the example data from FIGS.108A-C, it can be seen that while approximately half of responding UIDsselected “X” as their query response, an even higher proportion ofpre-event UIDs selected “X.” Knowing this, a marketer might decide tochange advertisements or products based on the fact that a highpercentage to pre-event, and likely very dedicated, attendees selectedoption “X” as the response to the query.

Turning to FIG. 109, there is illustrated a flowchart which details aprocess for creating pre-event-only UIDS for pre-event attendees that donot attend the actual event. In these situations, a special code orinput is displayed for pre-event attendees who do not have tickets tothe actual event. This code, along with a timestamp, is used in place ofa ticket with a seat number to register a mobile device and obtain aUID. In embodiments such as these, the pre-event-only UIDs make upanother group of UIDs that can be segmented, categorized, andcharacterized. The process starts at start block 10902 and moves tofunction block 10904. At block 10904, a message is displayed topre-event attendees which prompts them to start the application on theirmobile device and join the system. The process then moves to block10906, where a display, which could be a screen, a sign, or any otherkind of visual cue, presents an option for a “default ticket” number,which can be used by attendees who do not have actual tickets. Next, theprocess moves to decision block 10908, where the user decides which kindof ticket number to enter into the application for creating a UID. Ifthe attendee has an actual ticket number, the process moves to block10910, where normal processing occurs. The attendee enters the ticketnumber, which, along with a timestamp from the submission time, createsa unique UID. IF the attendee elects to use the default ticket numberfor attendees without actual tickets to the event, the process moves toblock function 10912. At block 10912, the default ticket number isentered by the user into the mobile device and combined with a timestampnoting the time of submission. The process moves to block 10914, wherethe timestamp is combined with the default ticket number to create a UIDfor the pre-event attendee. The process then ends at block 10916.

Although the pre-event-only attendees are able to participate in thesystem, it should be noted that these UIDs can be potentially created byanyone, even individuals who are not actually attending the event or thepre-event. Even though unique UIDs are created because of the inclusionof a timestamp, a single default ticket number, which is publiclydisplayed, is used by all of the non-event pre-event attendees, so thepossibility for fake or “spoofed” UIDs exists.

Turning to FIG. 110, there are illustrated tables illustrating how UIDscan be tagged as event attendee, pre-event attendee, or non-attendee(pre-event only) UIDs. Table 11002 is a dataset associated with aparticular UID. Table 11002 includes information associated with thatUID such as the UID number, the timestamp indicating when it wascreated, and the responses submitted by that UID. It also includeselements indicating if the UID is for an event attendee, a pre-eventattendee, or a pre-event-only attendee. Table 11004 is a tablerepresenting a dataset listing all of the UIDs that are pre-event UIDs,table 11006 is a table representing a dataset listing all of the UIDsthat are pre-event-only UIDs, and table 11008 is a table representing adataset listing all of the event UIDs. Using these tables, informationcan be filtered based on what group of UIDs the system operator wants toview information about. For example, a UID that is a pre-event UID willbe tagged as such in the UID dataset. That UID will also be listed inthe table 11004 which lists all of the pre-event UIDs. That way data forevery pre-event UID, event UID, or pre-event-only UID can be easilyretrieved by selecting the UIDs in the desired listing dataset.

Turning to FIG. 111, there is illustrated an example of a graph 11102depicting the cumulative number of responses for all UIDs, pre-eventUIDs, and pre-event only UIDs as a function of time. Line 11104represents the cumulative number of all UIDs that have submittedresponses during a time window for a given point in time during thattime window. Horizontal axis 11110 represents time during a responsetime window. Vertical axis 11112 represents the cumulative number ofUIDs that have submitted responses during the time window at a giventime. Line 11106 represents the number of pre-event UIDs that haveresponded as a function of time, and line 11108 represents the number ofpre-event-only UIDs that have responded during that time window as afunction of time. Like the graph 10702 in FIG. 107, graph 11102 allowssystem operators to analyze which type of UIDs are responding during aparticular response time window, and how fast each group is responding.The information provided by graphs such as graph 11102 is useful tosystem operators and marketers, as it allows them to better understandhow engaged different groups of UIDs are with the system for a givenquery.

Turning to FIG. 112A-C, there are illustrated three pie charts 11202,11204, 11206. In this example, a query is presented that has threepossible responses—“X,” “Y,” and “Z.” Each pie chart represents the UIDsthat submitted that response and displays the proportions of the UIDtypes. For example, chart 11202 shows that, for the UIDs that submitteda response of X, the proportion of UIDs that were event-only UIDs was alittle over half. The rest of the UIDs that responded with X were splitabout evenly between pre-event UIDs and pre-event-only UIDs. Chart 11204shows that, for the UIDs that submitted a response of Y, about half wereevent UIDs, and pre-event-only UIDs outnumbered pre-event UIDs. Finally,chart 11206 shows that for the UIDs submitting a Z response, the UIDswere about evenly divided between event UIDs, pre-event UIDs, andpre-event-only UIDs. Again, being able to create graphs such as graphs11202, 11204, 11206 provides system operators and marketers valuableinformation, since they will not only be able to see the reactions ofevent attendees to a query, but will also be able to see how differenttypes of attendees respond and compare to the responses of other typesof attendees and will be able tailor future advertisements or productsaccording to the opinions of the attendees they wish to target.

Turning to FIG. 113, there is illustrated an embodiment in which thetransient moments in a live event are analyzed. During live events,attendees often move about the event venue for any number of reasons,including going for concessions, using the restroom, or going to adesignated smoking section, such as an outdoor patio. Knowing whenattendees move about the venue, and to where they move, can provideuseful information to venue operators and to marketers, or other mediamessengers. Illustrated in FIG. 113 is a depiction of an event venue11302. Event venue 11302 has several different areas inside of it. Eventseating area 11304 is where event attendees 11306 normally sit or standwhile watching the event. Restroom area 11308 is an area having restroomfacilities. Concessions area 11310 is an area of event venue 11302having concession stands and other shops where attendees can purchasefood or gifts/souvenirs. Finally, smoking patio 11312 is an outdoorpatio with a designated smoking area. Most event venues are generallynon-smoking facilities, and so many venues have designated areas, suchas smoking patio 11312, that are outdoors or have separate ventilationfrom the rest of the venue and where event attendees 11306 can go tosmoke. Each of the areas 11304, 11308, 11310 and 11312 has displays11314 appropriate for displaying messages, advertisements, queries, orother visual cues. The displays 11314 in each area are viewable only byattendees in that area. For example, display 11314 a is viewable only byattendees in the event seating area 11304. Display 11314 c is onlyviewable by attendees in concessions area 11310. Display 11314 b isviewable only by attendees in restroom area 11308, and display 11314 dis viewable only by attendees in outdoor patio area 11312.

In operation, when a query is presented to event attendees, the samequery with the same response options is presented to all attendees inthe event venue 11302. However, for each area 11304, 11308, 11310, and11312, the displays have input options corresponding to the responseoptions that are different than the input options in the other areas.For example, in the situation depicted in FIG. 113, a query is presentedto attendees 11306 which has three possible responses. The query couldbe displayed on the displays 11314 or on other displays throughout thevenue 11302 but not depicted. Each of the displays 11314 displays threeinput options to attendees. In the event seating area 11304, the threepossible query responses correspond to the inputs of pressing “4,” “5,”and “6” on an attendee's mobile unit 110. In the outdoor patio area11312, however, those same three possible responses correspond to theinputs of pressing “A,” “B,” and “C” on an attendee's mobile unit 110.In the concessions area 11310, the responses correspond to inputs of“D,” “E,” and “F,” while the responses in the restroom area correspondto inputs “1,” “2,” and “3.” Once the query is presented to eventattendees, the response from any particular mobile unit 110 will providea good indication of which part of the event venue 11302 that user wasin when he or she submitted the response (some attendees may makeerroneous response inputs that do not correspond to inputs shown on thedisplay 11314 he or she saw, but this will number will likely be lowcompared to the overall number of responses). For example, if a userresponds to the query with an input of “4,” “5,” or “6,” then the userwas probably in the event seating area 11304 when he or she saw thequery and submitted a response. Or, if the user responds with a “D,”“E,” or “F,” the user was probably in the concessions area 11310 when heor she saw the query and responded. By analyzing the responses fromevent attendees, operators not only gather information about attendees'opinions related to the presented query, they also gather informationabout where in event venue 11302 event attendees 11306 were when theysee and respond to each query. Of course, the same display could bedisplayed in all areas with a banner on the non-main event displaysproviding a prompt such as “Press Z after your response to be entered towin a prize!”

Turning to FIG. 114, there is depicted a graph which helps illustratehow using assigning different response input options to query responsesin different areas of a venue can provide information on the movement ofevent attendees. Graph 11402 is a graph which depicts the query responseinputs received from an arbitrary UID, UID XXX. The horizontal axis11404 depicts time, increasing from left to the right of the axis 11404.Marked on horizontal axis are time markers T1, T2, T3, T4, T5, and T6.Each of these markers represents a time at which a query response wasreceived from UID XXX. Above each time marker on axis 11404 is theresponse input submitted by UID XXX at the corresponding time. Forexample, at time T1, UID XXX submitted a response input of “4.” At timeT3, UID XXX submitted a response input of “2.” At time T5, UID XXXsubmitted a response input of “C.” By correlating the submittedresponses with the response input options presented at each event area11304, 11308, 11310, 11312, it can be determined where the userassociated with UID XXX was when he or she submitted each response.

Turning to FIG. 115, there is illustrated a table 11502 that would befound in a relational database for correlating UID responses with eventvenue areas. In this example, table 11502 is for example Query #QQQ.Each query would have its own table. For Query QQQ, there are threepossible query responses—R1, R2, and R3. Each response corresponds to adifferent response input option, and a given response has differentresponse input options for each venue area 11304, 11308, 11310, 11312.In table 11502, the venue areas are designated I, II, III, and IV(corresponding to the outdoor patio area 11312, the concessions area11310, the restrooms area 11308, and the event seating area 11304,respectively). Each venue area has an associated column in table 11502which shows the response input options in that area 11304, 11308, 11310,11312 that correspond to each query response. For a given query, to findout where a user with a particular UID was located when he or shesubmitted a response, first the table 11502 corresponding to the correctquery must be located. Then, the response input is located in the table11502. The leftmost element of the row in which the response input islocated indicates the query response that was selected. The uppermostinput of the column in which the response input is located indicates thevenue area 11304, 11308, 11310, 11312 in which the user with that UIDwas located when he or she submitted the response. For example, if a UIDsubmitted a response input of “E” for Query QQQ, then the appropriatetable 11502 (the one depicted) is located. Then, finding the elementcontaining the input of “E,” it can be seen that the response was R2,and the UID was located in area II (the concessions area 11310).

Turning to FIG. 116A, there is illustrated a table 11602 whichcorrelates the response inputs from a UID to the location of that UID'suser. In this example, the table 11602 is for UID XXX, the same exampleuser as was used for FIG. 114. Table 11602 has three rows. The first row11604 has elements which indicate the query of that column as a functionof time. The second row 11606 indicates the response input submitted forthe query in that column, and the third row 11608 indicates the areathat corresponds with the response input in that column. For example,the first column indicates that for the query presented at time T1, UIDXXX responded with an input of “4.” By using a table 3902 such as theone depicted in FIG. 39, it is determined that a response input of “4”correlates with the event seating area 11304, as is indicated in thefirst element of row 11608. This means that at time T1, the user usingUID XXX was likely in the event seating area 11304. As another example,the for the query displayed at time T4, UID XXX submitted a responseinput of “D,” which correlates with the concession area 11310, meaningthat at time T4, the user of UID XXX was likely in the concessions area11310.

Turning to FIG. 116B, there is illustrated a timeline 11610 depictingthe locations of UID XXX as a function of time. In this example,timeline 11610 uses the same example UID and information as depicted inthe examples of FIGS. 114-116A. Timeline 11610 is constructed using theinformation compiled from table 11602 of FIG. 116A. By knowing thelocation of a particular UID at the time a response to a query wassubmitted, time-and-location information can be assigned to a UID. Usingtables such as those in FIGS. 39 and 116A, queries are associated withtimes. Each query has a time, response options, and response inputoptions associated with it. Each response input option for a querycorresponds to a particular venue area. Putting this informationtogether allows for the association of a time with a venue area. Thus,with the information compiled from table 11602, a timeline 11610 of thelocation of UID XXX can be constructed. Using the information, it can beseen that at times T1 and T2, UID XXX was in the event seating area11304. At time T3, UID XXX was in the restroom area 11308. At time T4,UID XXX was in the concessions area 11310. At time T5, UID XXX was inthe outdoor patio area 11312. Finally, at time T6, UID XXX was back inthe seating area 11304. The same process can be conducted for any UID.Being able to construct a timeline, such as timeline 11610, for a UIDallows the system operators to essentially track the movements of eventattendees.

Turning to FIGS. 117A-D, there are depicted four example graphs whichare used to analyze the movement of event attendees as a group. Eachgraph depicts the number of UIDs responding in a particular area of thevenue 11302. Graph 11710 depicts the number of responding UIDs in theevent seating area 11304 as a function of time. Graph 11720 depicts thenumber of responding UIDs in the outdoor patio area 11312 as a functionof time. Graph 11730 depicts the number of responding UIDs in theconcessions area 11310 as a function of time. Graph 11740 depicts thenumber of responding UIDs in the restroom area 11308 as a function oftime. By analyzing data from these graphs, the overall movements of theattendees as a whole can be determined. For example, at time T3, a largenumber of UIDs submitted responses from the seating area 11304. However,at time T4, the number of responding UIDs from the seating area 11304was much lower, while the number of responding UIDs in the concessionsarea 11310 was much higher. This would indicated that between times T3and T4, a large number of attendees moved from the event seating area11304 to the concessions area 11310. Knowing when attendees move todifferent parts of an event venue would allow system operators andmarketers to better tailor future messages. It would also allow venueoperators to determine if particular movements are related to conditionsor occurrences during the event. For example, if in a baseball stadium,the UID population of the concessions area 11310 typically rises at thetime that corresponds to the “7^(th)-Inning Stretch” portion of thegame, then it could be deduced that attendees like to visit theconcession stands during the 7^(th)-Inning Stretch. Knowledge of whenlarge numbers of attendees are likely to move to particular areas canalso help operators determine the best time to present certain messagesor advertisements. For example, if, by analyzing locations of UIDs,system operators know the most likely time for a spike in the UIDpopulation at concession stands, then prior to that time, they mightpresent several advertisements for food and beverages that can bepurchased at the concession stands.

Turning to FIG. 118, there is illustrated an example of an embodimentwherein the system described hereinabove is embedded within anotherapplication on the user's mobile unit 110, rather than being astandalone program. The reason for this is that there are severalpopular third-party mobile applications, such as Facebook and Twitter,which are already familiar to users, advertisers, sports teams andleagues, and venue operators. In these embodiments, the functionality ofthe system is simply built into the third-party mobile application aspart of the third-party application's code or as a plug-in. The mobileuser launches the system described hereinabove by accessing thefunctionality from within the third-party application. In many cases,the user will see the additional functionality as simply another part ofthe overall third-party application.

FIG. 118 illustrates an example of the main screen, or home screen, thatappears when a mobile user launches the third-party application on amobile unit 110. The home screen has several virtual buttons 11810 thatcan be activated via a touch-screen interface. Each of these virtualbuttons 11810 activates a different feature of the third-partyapplication or takes the user to a different part of the application.For example, activating button 11810 a would display a list of themobile user's social connections, button 11810 b would displaythumbnails of the mobile user's photos he has uploaded to the socialnetwork, and button 11810 c would display a selection of news articles.Button 11810 d activates the functionality of the system describedhereinabove and displays a new screen 11820, which prompts the user toregister his or her device with the venue's wireless network, similar tothe process depicted in FIG. 9. The system then proceeds normally in thesame way as is described hereinabove, only as a part of the third-partyapplication, rather than as a separate, stand-alone application.

Turning to FIG. 119, there is illustrated a flowchart which depicts theprocess for using a third-party application to implement the attendeeengagement system. The process starts with start block 11902 andproceeds to function block 11904. At block 11904, the attendee launchesthe third-party application, such as a social networking application onhis mobile unit 110. The process moves to block 11906, where, if needed,the user logs into the social application. Next, at function block11908, the attendee activates the attendee engagement systemfunctionality of the third-party application, which displays theregistration screen for the attendee engagement system. Next, at block11910, the mobile unit 110 accesses the venue wireless network on whichthe system will operate. The process moves to block 11912, where theattendee enters registration information, such as a ticket number orsection/row/seat information. Next, at function block 11914, the mobileunit generates a UID with the input information and submits theinformation to the venue wireless network, completing the registrationprocess. The process then ends at block 11916. The third-partyapplication has now used its own built-in functionality or plugin toregister the user with the venue's wireless network, and the attendeecan now use the attendee engagement system in the same was as isdescribed hereinabove.

The attendee engagement system can be embedded in various types ofthird-party applications. In some embodiments, the third-partyapplication will be a social networking application such as Facebook,Twitter, or Pinterest. The system could also be embedded within venue,team, or sports league-centric mobile application, such as NFL Mobile,the NBA mobile application, the FIFA Official App.

In some embodiments, the login information, handle, or user ID of thethird-party application is used as part of the UID. For example, if theattendee engagement system is embedded in the Twitter mobileapplication, then the user's Twitter handle is used along with ticket orseating information and a timestamp to generate a UID.

Turning to FIG. 120, there is illustrated an embodiment wherein a user'squery responses are posted to his social media sites. One issue with thesystem described hereinabove is that the queries and responses are onlypresented to individuals in the event venue or otherwise participatingengaging with the system. One way to address this issue is to have thequeries and responses posted to social media profiles of system users.When a query is presented in the event venue, the user selects aresponse on his MU 110. The user's social media I.D. information islinked to the user's UID such that after the user submits a response,the response and its corresponding query can be posted to the user'ssocial media site. This way, anyone who can see the user's social mediaprofile can see that the user used the system to submit a response andcan see what response he submitted. Advertisers, or anyone else with amessage they wish to have distributed, will not appreciate this, astheir message will not only be displayed on social media websites, butwill also be, in a way, “endorsed” by the social media users whoselected the response that resulted in the message or advertisementbeing posted.

Shown in FIG. 120 is a query 12002 that is presented on a display on anevent venue. In this example, the query asks the event attendee whichmanufacturer, in the attendee's opinion, makes the best trucks. It givesthree response options for the user to select. In this example, the userselected Chevy as the best truck manufacturer. Rather than the resultsof the vote simply being shown on a display in the event venue, thechoice of each attendee can be displayed as a post on their social mediapage. Screen 12004 is an example of such a post. Screen 12004 representsa computer or mobile device screen view shown to users of a social mediawebsite. It includes several options 12008 for accessing different partsof the social media site, as well as several “posts” 12012 from varioussocial media users. Continuing with the example wherein the userselected Chevy as the answer to the query, that selection now appears asa post 12006 on the user's social media page. The post 12006 includesinformation relating to the reader both the original query and theuser's response to the query. The post 12006 can also include a link12008 to a webpage related to a product or service mentioned in thequery or the user's selected response. In some embodiments, the post12006 will also include a video or video link 12010 related to a productor service included in the query or query response.

Turning to FIG. 121A, there is a diagram illustrating how a UID and itscorresponding social media information are associated, as well as howquery and response data are posted to social media sites. Multiplemobile units 110 are in communication with a local office 120. When theuser is entering ticket and/or section/row/seat information into theapplication on the mobile unit, the user will also enter social mediaI.D. information. This information includes usernames and loginpasswords. In some embodiments, this information is entered each timethe user registers his MU 110 at an event venue. In other embodiments,the social media I.D. information is store in the application afterbeing initially input by the user, or is obtained through other socialmedia applications installed on the user's mobile unit 110. The MU 110then generates a UID and transmits that UID, along with the user'ssocial media information (which in some embodiments is part of the UID),to the venue's local office 120 via the venue's wireless network. UIDsare stored on servers in the local office 120, along with the socialmedia information from the UIDs that also submitted such information.Local office 120 stores the UID and social media information inrelational databases 12104 which associate each UID with its socialmedia information. As the live event progresses, users will submit queryresponses to the local office 120. As these responses are received, theyare associated with the UID as described hereinabove. Each venue's localoffice 120 is in communication with the remote central office 126 via anexternal network such as the internet. After the end of the live event,the local office 120 will transmit relational databases 12104, alongwith the responses that are associated with each UID, to the remotecentral office 126. In some embodiments, however, UID and social mediainformation are sent to the remote central office 126 as soon as theyare created, and each response is transmitted to the remote centraloffice 126 as soon as it is received by the local office 120. Either atthe remote central office 126 at local office 120, query responses areassociated with queries based on the timestamps of the queries, in themanner as described hereinabove. Remote central office 126, using therelational database tables 12104, then associates UID, query, andresponse information with the social media information provided by theuser. Using the social media information from UIDs contained in thetables 12104, along with databases of pre-constructed posts thatcorrespond to each query and response, the remote central office 126then constructs social media posts based on the queries and responsesreceived from the various local offices 120. The remote central office126 has the ability to communicate through the internet with varioussocial media websites 12102. Once the social media posts areconstructed, the remote central office 126 uses the social mediainformation to access the appropriate social media sites 12102 andsubmit the posts on to the pages of the users' UIDs.

Turning now to FIG. 121B, there is illustrated a variation of theembodiment depicted in FIG. 121A. In FIG. 121B, like in FIG. 121A, themobile unit 110 sends UID and query response information to the localoffice 120 via the venue's wireless network. In this embodiment,however, the MU 110 does not send any social media I.D. information tothe local office 120. Instead, the social media information is sentdirectly to the remote central office 126. This can be accomplished in anumber of ways, for example, via the venue's Wi-Fi network (then to theremote central office 126 via the internet without going through thelocal office 120), or via cell-phone towers 12106 which carry wirelessdata networks used by mobile phone carriers. In one aspect of thisembodiment, when a user registers his MU 110 at the event venue, the MU110 transmits the UID and social media I.D. information to the remotecentral office 126. Later, when the local office 120 sends UID, query,and response information to the remote central office 126, the UIDs andsocial media I.D.s are associated at the remote central office 126.Using this aspect, the user would have to send UID and social mediainformation to the remote central office 126 each time the userregisters at an event venue. In another aspect, the user's MU 110submits the UID information to local office 120, and the UID and socialmedia account information to the remote central office 126. However,along with the UID and social media information, the MU 110 transmits astatic, device-specific, unique identifier—such as a mobile phone'sInternational Mobile Subscriber Identity (IMSI) number—to the remotecentral office. The remote central office 126 then the associates thestatic identifier with the submitted social media account informationand stores this association in a database. That way, at future events,the user will only have to submit his IMSI, or other static identifier,and UID to the remote central office 126, since the office 126 willalready have the social media information stored on a server andassociated with the static identifier.

Turning to FIG. 122, there is depicted a flowchart illustrating theprocess by which social media account information is associated with aUID and its responses. The process begins at start block 12202 andproceeds to function block 12204. Here, the user launches theapplication on his MU 110. Next, at block 12206, the applicationpresents the registration screen to the user, prompting him to enter hisregistration and social media account information. Next, at block 12208,the application causes the MU 110 to find and access the event venue'swireless network. At block 12210, the user enters his registrationinformation, which can include a ticket number or section/row/seatinformation. The process moves to block 12212, where the user enters hissocial media network account information into the application. Next, atblock 12214, the user confirms that the input information is correct,and the application on the MU generates a UID and submits it to theevent venue's local office 120 via the venue's wireless network. Atfunction block 12216, the application submits the social media accountinformation to either the local office 120 or the remote central office126. Variations of this block are illustrated in FIGS. 123A-123C. Next,at block 12218, the MU transmits its query responses to the local office120 via the venue's wireless network. After the live event has ended, atblock 12220, the local office 120 transmits UID and response informationto the remote central office 126. In some embodiments, however, queryresponse information in sent from the local office 120 to the remotecentral office 126 before the end of the live event, at multipledifferent times, or even each time a response is received by localoffice 120. Next, at block 12222, once the UID, response, and socialmedia account information are transmitted to the remote central office126, the UID and response information are associated and stored with theappropriate social media account information. The association processthen ends at block 12224.

Referring to FIGS. 123A-123C, there are a number of different ways thatsocial media information can be collected by the system from the usersMU 110, as depicted in block 12216 in FIG. 122. Turning to FIG. 123A,there is illustrated a process wherein the UID and social media data aretransmitted to the remote central office 126 without going through thelocal office 120. The process picks up at function block 12214, thenproceeds to decision block 12302. Here, if the user has previouslyregistered his social media information with the system at the currentvenue, or even another venue using the same system, then the processflows to function block 12306, where the user's MU 110 transmits its UIDand its static identifier (such as an IMSI) to the remote central office126. Since the user has previously registered his social mediainformation in conjunction with his MU's 110 static identifier, thesystem can simply look up the social media information based on theprovided static identifier and link it to the provided UID. The processthen moves to block 12218 and continues with the process of FIG. 122.If, at block 12302, the user has not previously registered his socialmedia information with the system, the process moves to block 12304,where the MU 110 transmits the UID, the social media information, andthe static identifier (such as an IMSI) to the remote central office126, where the social media information and static identifier are storedin a database linking them together. In both blocks 12304 and 12306,when information is transmitted from the MU 110 to the remote centraloffice 126, it can be transmitted via cell-phone data networks, LTEnetworks, event venue wi-fi networks, or any other network that allowthe MU 110 to transmit data through an external network such as theinternet to remote central office 126.

Turning to FIG. 123B, there is illustrated another embodiment offunction block 12216 from FIG. 122. In this embodiment, the processpicks up from block 12214 of FIG. 122 and moves proceeds to block 12310.At block 12310, the MU submits the social media information and UIDdirectly to the remote central office 126. At the remote office 126, thesocial network information and UID are linked together in a database sothat query responses can be associated with the correct social mediaaccount information. The process then moves to block 12218, where theprocess depicted in FIG. 122 picks up again.

Turning to FIG. 123C, there is illustrated another embodiment of block12216 from FIG. 122. In this embodiment, the social network informationis submitted to the local office 120 instead of the remote office 126.The embodiment picks up at block 12214 from FIG. 122 and continues tofunction block 12312. At block 12312, the MU 110 submits the socialmedia account information input by the user to the local office 120 viathe venue's wireless network. Since the MU 110 has already generated aUID and submitted it to the local office 120, the UID and social mediaaccount information can be stored and linked together at the localoffice 120 instead of at the remote central office 126. The process thenmoves to block 12218 where it rejoins the process depicted in FIG. 122.

Turning to FIG. 124, there is illustrated a flowchart depicting anexample of the process for generating a social media post for a userbased on a query and the user's response to the query. The processstarts at start block 12402 and moves to function block 12404. At block12404, a query is presented to the event attendees via the event venue'svisual display. The process moves to block 12406, where the MU 110submits a response to the query to the venue's local office 120 via thevenue's wireless network. Next, at decision block 12408, if the socialmedia account information is stored in the local office 120, the processmoves to block 12410, where the UID, queries, and response informationare associated with the social media information for the UID. Theprocess then moves to block 12412, where the UID, query, response, andsocial media information are all forwarded to the remote central office126. The process the moves to block 12418, where query, response, andsocial media information for the UID are compiled together to generate asocial media post indicating the query and the user's response to thequery. If, at block 12408, the social media account information has beenstored at the remote central office 126, the process moves to functionblock 12414, where the local office 120 forwards the UID and query andresponse information to the remote central office 126. Next, at functionblock 12416, the UID, queries, and responses are associated with thecorrect social media account information. The process the moves to block12418, where query, response, and social media information for the UIDare compiled together to generate a social media post indicating thequery and the user's response to the query. At block 12420, remotecentral office 126 uses the social media account information associatedwith the UID to log into the user's social media network via theinternet and submit the post generated at block 12418. Next, at block12422, the system determines the appropriate advertisement or message toappend to the post. The message or advertisement can be retrieved from adatabase of advertisements based on the response to the query submittedby the UID. This database could be on a server in the remote centraloffice 126, or it could be on a third-party server, such as the creatorof the message or advertisement. The message or advertisement can takeany number of forms, including a simple web-link, a verbal descriptionof a product or service, an image, an audio message, or even a video.Next, at block 12424, the remote central office 126, using the UID'sassociated social media account information, transmits the message viathe internet to the social media site for it to be appended to theuser's post. In some embodiments, this step occurs at the same time theoriginal post is sent to the social media site such that blocks 12420and 12424 occur as one step instead of two separate steps. The processthen ends at block 12426.

Turning to FIG. 125, there is illustrated an embodiment in which anadditional message is appended to the social media post. This additionalmessage is related to the event or the venue at which the event istaking place and is based on information from the UID. By adding such amessage, the venue or event can also be advertised in addition towhatever is related to the actual query or query response. For example,if the event is a sports game, the message could be an advertisement forone of the teams playing at the event. Or it could be an advertisementfor the sports league to which the teams belong. Or, the message couldbe an advertisement for the event venue itself. FIG. 125 depictsdifferent examples of venue/event-specific messages that can be appendedto a social media post. In the example, the event is a football game ata stadium. Message 12502 represents a video advertisement related to theevent venue. In this example, message 12502 is a message for thefictional Monroe Stadium that could appended to the social media post ofa user at the example football game. The message 12502 might givedetails of the venue, such as its geographic location, the major eventsthat occur there, the sports teams that are based there, or any othertype of information that venue owners or operators might want to beadvertised or disseminated. Message 12504 is a message that relates tothe type of event the user is at. In this example, the message is avideo for the specific sports league (a football league) the event is apart of. However, the message could be related to the type of event ingeneral, such as an advertisement for a series of concerts or otherartistic or sporting events, or to other events related to a sponsor ofthe current event. Message 12506 is a message that relates to theperformers or players at the event. In this example, message 12506 is anadvertisement for the sports team playing at the event. The messagecould also related to specific players on the team, or, if the event isnot a sporting event, to specific actors, performers, groups, or anyother individuals (or objects) featured in the event.

Turning to FIG. 126, there is illustrated a flowchart depicting theprocess by which an event-based or venue-based video is appended to asocial media post. The process starts at start block 12602 and proceedsto function block 12604. At block 12604, the system determines which thevenue the user is attending, based on the information stored in thevenue data field of the user's UID. After extracting the informationfrom the venue data field, the remote central office 126 accesses adatabase which relates venue data field information to correspondingevent venues. In some embodiments, the remote central office 126 alsoextracts the timestamp information from the UID. By doing this, thecentral office 126 can access a database which relates venue data fieldinformation and UID timestamp to a specific venue AND live event, sincean event can be defined by a venue and event time (assuming no more thanone event using the system is taking place at each venue at a giventime). Either way, after the central office 126 determines which venueand/or event the UID originated from, the process moves to functionblock 12606. At block 12606, the remote central office 126 accesses adatabase which stores video messages for appending to social mediaposts. Each video is associated with at least onevenue/event/team/league/etc. Using a database which relates the UIDvenue location information and/or timestamp information to the videoswithin the video database, the remote central office 126 selects andretrieves the appropriate video for the UID in question. The processthen moves to function block 12608, where the central office appends thevenue/event video to the social media post in same manner as describedhereinabove in FIG. 124. The process then ends at block 12610. Theexamples described hereinabove with respect to FIGS. 125 and 126 usevideo messages as the messages being appended to social media posts.However, the messages can be video messages, audio messages, textmessages, images, or any combination of the above.

Referring now to FIG. 127A, some embodiments use section, row, and seatnumber information to construct UIDs. This addresses a number of issues.First, it allows each event attendee to have a unique UID, since everyattendee will have a ticket with different section/row/seat information.Also, construction of a UID with seating information of the userassociated with the UID allows for determining the geographic locationof the attendee. This can be useful in instances wherein prizes areawarded to attendees for submitting responses. Having the seatinglocation of the attendee will allow the prize to be delivered to theuser during the event (the delivery of the prize can also be displayedon video displays, increasing the engagement of event attendees with themessaging system). In FIG. 127A, there is illustrated a diagrammaticview of a generic UID data structure 12710. The UID data structure 12710has multiple generic data fields 12712, 12714, 12716, 12718, 12720. Eachdata field is populated with data from a different type ofcategorization. Data structure 12730 is an example of a form of datastructure 12710 that uses section/row/seat information to create a UID.Data structure 12730 is made of the several data fields, just like datastructure 12710. Each field contains a different type of identifyingdata associated with an event attendee. In these embodiments, the firstdata field 12732 contains some kind of data identifying the event venue.The second data field 12734 contains data associated with the sectionnumber of the event attendee's ticket. The third data field 12736contains data associated with the row number of the ticket. The fourthdata field 12738 contains data related to the seat number of the ticket.Finally, the fifth data field 12740 contains a timestamp created at thetime the UID information is submitted by the mobile unit 110.

In practice, the event attendee enters the section, row, and seatinformation into the application on the mobile unit 110. Thisinformation is used to populate the data fields 12734, 12736, and 12738,respectively. The attendee might also enter a code or other informationprovided by the event venue into the application to populate the firstdata field 12732 with information related to the specific event venue.The last field, the timestamp data field 12740, is populated when themobile unit 110 has collected the rest of the information needed forconstructing the UID data structure 12730 and submits the data structureto the venue wireless network. The timestamp data field 12740 is usefulto ensure that multiple mobile units do not have the same UID. Forexample, suppose event attendees enter the same section/row/seatinformation into the application—the second attendee accidentallyentering the wrong seat number. Without the timestamp data field 12740,there would be no way to distinguish the two mobile units 110 apart,since they would have entered the same section/row/seat information andthus created the same UID. However, with a timestamp data field 12740,the venue system will be able to distinguish the mobile units 110 apartfrom each other, even with identical section/row/seat information, sinceit is highly likely that the UID creation timestamps will be at leastslightly different from each other. The system will then understandthat, even though the two set of identical section/row/seat informationwere submitted, the submissions actually came from different mobileunits 110.

Turning to FIG. 127B, there is illustrated an embodiment with a slightlydifferent format for the UID, whereby the information for the venue datafield is not entered by the event attendee. Instead, the venue datafield is appended by the venue central office to the UID data structuresupplied by the attendee's mobile unit 110. FIG. 127B illustrates datastructure 12750, which represents a UID data structure comprised of datafields 12752, 12754, 12756, and 12758. Data fields 12752, 12754, and12756 contain information input by the attendee and are associated withthe attendee's ticket section, row, and seat, respectively. Data field12758 is the timestamp indicating the time at which the UID informationwas submitted to the venue's wireless network. In these embodiments, theUID, comprised of data fields 12752, 12754, 12756, and 12758 issubmitted to the venue's wireless network. The UID information isforwarded to the venue's central office. At the central office, thesystem then creates a modified UID—referred to here as UID′ 12760—whichalso includes a data field 12762. Data field 12762 includes dataidentifying the venue at which the UID was created. Automaticallyappending venue information to a UID instead of relying on the eventattendee to enter that information is beneficial for a number ofreasons. First, it eliminates one opportunity for error on the part ofthe attendee. If the attendee enters the incorrect venue information,then comparisons between multiple venues will not be accurate. Also,since every attendee in the venue is in the same venue, and will thushave the same venue information appended to his or her UID datastructure 12750, it will be very easy to simply have the same data fieldwith the same information appended to every UID that is created within avenue. The new data structure, UID′ 12760, now contains all of theidentifying data for within the venue and also contains an additionaldata field 12762 which can be used if UIDs and responses are comparedamong different venues.

Turning to FIGS. 127C-E, there are depicted three pie charts whichillustrate how, by sorting UIDs based on the venue informationassociated with the UIDs, UIDs and response data can be compared acrossvenues. In many instances, a query or set of queries will be presentedto audiences at multiple venues. Being able to compare the responses ofaudiences at different venues will be useful for a number of reasons.First, if the venues surveyed have similar events—the different venuesare both baseball stadiums, for example —, and the attendee populationsare expected to be demographically similar, then comparing data frommultiple venues will provide a more representative sampling of theattendee populations. Second, different venues might have differentattendee demographics, for example if one venue is a concert hall, andthe other venue is a soccer stadium. In these cases, presenting the samequery to the different venues will allow for the determination of howlarge samples of attendees of different demographics respond to the samequery or queries.

Illustrated in FIGS. 127C-E are three pie charts which depict theresponses of responding attendees at three different venues to the samequery, which has three possible responses, “A,” “B”, or “C.” Chart 12770shows the responses from attendees in Venue I, chart 12772 showsresponses from attendees in Venue II, and chart 12774 shows responsesfrom attendees in Venue III. As can be seen from chart 12770, about halfof the responding attendees chose “A” as their response, while the otherhalf were about evenly split between “B” and “C.” As depicted in chart12772, the attendees in Venue II chose responses “A,” “B,” and “C” inabout even proportions. Finally, as depicted in chart 12774, theattendees in Venue III overwhelmingly chose response “C” over responses“A” and “B.”

Illustrated in FIGS. 127F-H are examples of three pie charts which areused to compare the demographic make-up of responding attendees atdifferent event venues. By sorting UIDs based on the venue-identifyinginformation contained in their data structures, the demographics ofattendees at different venues can be compared to each other and analyzedwith regard to response information, such as is depicted in FIGS.127C-E. Pie charts 12776, 12778, and 12780 depict the ratio ofresponding attendees that are male and female, with each chart being forthe attendees at a specific venue. Chart 12776, for example, shows thatat Venue I, the responding attendees were about evenly split betweenmales and females. Chart 12778 shows that at Venue II, most of theresponding attendees were female. Chart 12780 shows that at Venue III,most of the responding attendees were male. This information can beuseful for comparing the demographic make-up of audiences at differentvenues. Demographic information from venue-sorted UIDs can also becombined with response information from venue-sorted UIDs (like thatdepicted in FIGS. 127C-E) to produce an even more accurate picture ofthe opinions and preferences of attendees. Attendees could be sorted notjust by gender, but also by race, age, political affiliation, or anyother demographic categorization.

Turning to FIG. 127I, there is illustrated a flowchart that depicts theprocess for automatically assigning venue information to a UID. Theprocess starts at start block 12782 and moves to function block 12784,where the event attendee launches the application on his mobile unit110. The process moves to block 12786, where the application uses themobile unit's wireless functionality to find and access the venue'swireless network. The process next moves to function block 12788. Here,the event attendee enters ticket section, row, and seat information intothe application on the mobile unit 110. At block 12790, the informationentered by the attendee at block 12788 is sorted into data fields thatwill partially make up the mobile unit's UID. When the event attendeesubmits the information, the UID is created when data fields arecombined with one last data field generated from a time stamp indicatingthe time at which the section/row/seat information was submitted. Thenewly created UID is submitted to the wireless network. At block 12792,the system receives the UID at the venue's central office and uses adatabase to look up venue identification information related to thevenue at which the system is operating. The process moves to functionblock 12794, where the venue identification information is appended tothe received UID, thus creating a UID with venue-identifying informationincluded in it. The process ends at block 12796.

Turning to FIG. 128, there is illustrated an example of anotherembodiment, this one using an existing social network I.D. as part ofthe UID. Many event attendees have pre-existing accounts with one ormore online social media networks, such as Facebook, Twitter, Instagram,or LinkedIn. Integrating an event attendee's social network I.D. intothe UID for his or her mobile unit 110 adds another identifying featureto the attendee's UID. For example, if an attendee's social network I.D.is part of the UID, then all of the information compiled from that UID'sresponses can later be compiled with information from the attendee'ssocial network account to provide an even better assessment of theopinions and preferences of that attendee. Also, if the mobile surveysystem described hereinabove is integrated within a social media mobileapplication, rather than existing as a separate, standalone application,then the social media I.D. of the user is something that is readily andeasily accessible to be integrated into the UID without any extra efforton the part of the event attendee.

In FIG. 128, there is illustrated a data structure 12802. This datastructure 12802 is similar to the data structure 12730 depicted in FIG.127. The data structure 12802, however, contains an additional datafield, data field 12812. Data field 12812 is similar to the other datafields 12804, 12806, 12808, 12810, except that instead of containingdata related to the attendee's geographic seat location, data field12812 contains information associated with the attendee's existingsocial network I.D. For example, this information could be in the formof the attendee's social network username, an email address associatedwith the social network I.D., an account number, or a designation forthe social network I.D. used internally by the online social network.

Turning to FIG. 129, there is illustrated how, in some embodiments, theapplication allows the user to input a social network I.D. from one of aselection of multiple possible social networks, since all attendees willprobably not have accounts with the same social networks. In theseembodiments, there is another data field in the data structure 12802which is used to indicate which social network is associated with thesocial network I.D. being used. Registration menu 12902 is an example ofwhat would be shown on the screen of a mobile unit 110 during theregistration process, similar to that which is described in FIG. 11.Menu 12902 has input fields 12904 for section, row, seat, and socialnetwork I.D. information. The information input into fields 12904 willbe used to construct data structure 12802. This embodiment, however,also has a dropdown menu 12906, which allows the user to select thesocial network that corresponds to the social network I.D. he entered.The selection of a social network from dropdown menu 12906 will be usedto fill in the additional social network data field in data structure12802.

Illustrated in FIG. 130 is a view of a data structure 13002, which islike the data structure 12802 described in FIG. 128, except that datastructure 13002 incorporates data associated with which social network asocial network I.D. is used for, as described in FIG. 129. Data fields13004, 13006, 13008, 13010, 13012, and 13016 are associated with thevenue, the section, the row, the seat, the social network I.D., and thetimestamp, respectively. Data field 13014 contains data identifying thesocial network selected by the user from the dropdown menu 12906.

Referring to FIG. 131, there is illustrated an example of an embodimentwhich uses a media content caching server to store media messages. Whena media message, such as a video is appended to a user's social mediapost, the message can be directly uploaded to the social media site.However, this would be inefficient, since the same message will likelybe uploaded multiple times to multiple users' social media pages. Oneway to address this concern is to have media messages stored on acontent cache server which is accessed by the social media website,instead of uploading the same media message multiple times for each userwhose social media posts includes the same message. Media messages,especially videos, tend to be fairly large files when compared toordinary text or single images. The types of media messages that aretypically used by the present disclosure are also typically staticmessages, as opposed to dynamic messages that require frequent updating(as would be the case for media messages delivering news headlines). Thecombination of large file size and relatively static content means thatfile caching can greatly increase the efficiency of uploading andstoring these messages.

Turning back to FIG. 131, social media page 13102 is the social mediapage of a system user as seen in a web browser for a computer of amobile application for a mobile device. Media message 13104 is a mediamessage, in this case, a video, that has been appended to a post on theuser's social media page 13102. Media server 13106 is a media server onwhich a large number of media messages 13110 are cached. Link 13108 is ahyperlink on the social media page 13102 that is linked to the internetaddress of a file 13110 containing the media message 13104 on the mediaserver 13106. In operation, when a media message 13104 is appended to asocial media post, the media message 13104 is not actually uploaded tothe user's social media page 13104. Instead, a file 13110 containing themedia message is uploaded to the media server 13106 (unless the file13110 has already been uploaded to the media server 13106). When a useraccesses the media message 13104 on the social media page 13102, thebrowser or application uses link 13108 to send a request through theinternet 13112 to media server 13110 to download or stream the correctmessage file 13110 straight from the server to the browser orapplication, rather than from the social media page 13102 to the browseror application. The media message 13104 is presented as part of socialmedia page 13102, even though the message 13104 actually originated onmedia server 13110. The media message 13104 can be presentedautomatically when the social media page 13102 is loaded onto thebrowser or application, or the media message 13104 can presented onlywhen the user specifically accesses it through hyperlink 13108. In someembodiments, the hyperlink 13108 might appear to be part of a previewimage for message 13104, such that it is not apparent to the socialmedia user that the media message 13104 is being stream or downloadedfrom a separate media server 13106 rather than from the social mediasite's own servers. Once the media message file 13110 for a mediamessage 13104 is uploaded to media server 13106, any time that message13104 is appended to another user's social media page 13102, the postwill include the same link 13108 to the same file 13110 on the mediaserver 13106. This way, a media message 13104 which is appended tomultiple users' social media pages 13102 will still result in only onefile 13110 being uploaded and stored on media server 13106.

Turning to FIG. 132, there is illustrated a depiction of the embodimentshown in FIG. 131 being used by multiple social media users. Posts13202, 13204, 13206, 13208, 13210, 13212 are social media posts on thepages of different social media users. Each post 13202, 13204, 13206,13208, 13210, 13212 includes a media message 13104 and a hyperlink 13108which links through internet 13112 to a media file 13110 on media server13106. Note that some of the posts include the same media message 13104as other posts. For example, media posts 13202, 13206, and 13212 allinclude the same media message 13104, Video-C. Post 13204 includes mediamessage 13104 Video-B. Post 13208 includes Video-A as its media message13104, and post 13210 includes Video-D as its media message. Like theembodiment depicted in FIG. 131, each of the posts 13202, 13204, 13206,13208, 13210, 13212 includes a link 13108 to the appropriate mediamessage file 13110 stored on media server 13106. Since posts 13202,13206, and 13212 all include Video-C as their appended media messages13104, instead of storing three copies of file 13110 on server 13110,all three posts 13202, 13206, and 13212 link to the same file 13110 onmedia server 13106 with the same link 13108. Since it is possible thathundreds or even thousands of media posts will contain the same mediamessage 13104, linking to the same file 13110 will drastically reducethe amount of data stored on media server 13110.

Turning to FIG. 133, there is illustrated a flowchart depicting theprocess of storing a media message on a media server and serving it tosocial media users. The process begins at Start block 13302 and thenproceeds to function block 13304. Here, the media messages which can beappended to social media posts are uploaded to a media server, such asthat described hereinabove in reference to FIGS. 131 and 132. The mediamessages are uploaded by the media message producers or by the systemoperators. The process then moves to function block 13306. When a mediamessage is uploaded to the media server, the media server generates aunique URL link for that media message and provides it to the rest ofthe system for use in linking the message in social media posts to thefile on the server. The process then moves to block 13308. At block13308, a social media post is generated by the system, and a linkcontaining the message file URL is appended to the social media post.The process moves to function block 13310, where a social media useraccesses the media message file by using the URL provided in the socialmedia post. This could be by clicking on a link on the social mediapage, pressing a virtual “play button” on a preview image of a video, bythe mobile application or browser automatically using the URL to accessthe media message file on the media server. At function block 13312, themobile application or browser downloads or streams the media messagefrom the media server at the URL location and presents the media messageto the social media user as part of the social media post. The processthen ends at block 13312.

Turning to FIG. 134, there is illustrated an example of an embodimentwherein the system described hereinabove is embedded within anotherapplication on the user's mobile unit 110, rather than being astandalone program. The reason for this is that there are severalpopular third-party mobile applications, such as Facebook and Twitter,which are already familiar to users, advertisers, sports teams andleagues, and venue operators. In these embodiments, the functionality ofthe system is simply built into the third-party mobile application aspart of the third-party application's code or as a plug-in. The mobileuser launches the system described hereinabove by accessing thefunctionality from within the third-party application. In many cases,the user will see the additional functionality as simply another part ofthe overall third-party application.

FIG. 134 illustrates an example of the main screen, or home screen, thatappears when a mobile user launches the third-party application on amobile unit 110. The home screen has several virtual buttons 13410 thatcan be activated via a touch-screen interface. Each of these virtualbuttons 13410 activates a different feature of the third-partyapplication or takes the user to a different part of the application.For example, activating button 13410 a would display a list of themobile user's social connections, button 13410 b would displaythumbnails of the mobile user's photos he has uploaded to the socialnetwork, and button 13410 c would display a selection of news articles.Button 13410 d activates the functionality of the system describedhereinabove and displays a new screen 13420, which prompts the user toregister his or her device with the venue's wireless network, similar tothe process depicted in FIG. 9. The system then proceeds normally in thesame way as is described hereinabove, only as a part of the third-partyapplication, rather than as a separate, stand-alone application.

Turning to FIG. 135, there is illustrated a flowchart which depicts theprocess for using a third-party application to implement the attendeeengagement system. The process starts with start block 13502 andproceeds to function block 13504. At block 13504, the attendee launchesthe third-party application, such as a social networking application onhis mobile unit 110. The process moves to block 13506, where, if needed,the user logs into the social application. Next, at function block13508, the attendee activates the attendee engagement systemfunctionality of the third-party application, which displays theregistration screen for the attendee engagement system. Next, at block13510, the mobile unit 110 accesses the venue wireless network on whichthe system will operate. The process moves to block 13512, where theattendee enters registration information, such as a ticket number orsection/row/seat information. Next, at function block 13514, the mobileunit generates a UID with the input information and submits theinformation to the venue wireless network, completing the registrationprocess. The process then ends at block 13516. The third-partyapplication has now used its own built-in functionality or plugin toregister the user with the venue's wireless network, and the attendeecan now use the attendee engagement system in the same was as isdescribed hereinabove.

The attendee engagement system can be embedded in various types ofthird-party applications. In some embodiments, the third-partyapplication will be a social networking application such as Facebook,Twitter, or Pinterest. The system could also be embedded within venue,team, or sports league-centric mobile application, such as NFL Mobile,the NBA mobile application, the FIFA Official App.

In some embodiments, the login information, handle, or user ID of thethird-party application is used as part of the UID. For example, if theattendee engagement system is embedded in the Twitter mobileapplication, then the user's Twitter handle is used along with ticket orseating information and a timestamp to generate a UID.

Turning to FIG. 136, there is illustrated an embodiment in which asocial media user inputs a social media I.D. of the event venue into hismobile device, designating that the individual is attending a live eventat the venue. Many event venues have profiles or “handles” registeredwith various social media networks, such as Twitter, Facebook, orInstagram. Many of these social media platforms allow users to“check-in” at locations. In these embodiments, by “checking-in” at anevent venue via a social media account, the user notifies the socialmedia network that he is attending the event. This allows the socialmedia network to sort statistics from that user's social media accountinto a group specifically designated as being at the event.

Referring back to FIG. 136, there is illustrated MU 110, which theattendee uses to check-in at the event. The MU 110 is running a socialmedia application with which the attendee has an account. Alternatively,the MU 110 could also be running a web browser which is logged into thewebsite of the attendee's social media account. The attendee checks-inat the event by entering the social media name, label, or handle of theevent venue into the social media application or webpage. By doing this,the social media network used by the event attendee knows, with somedegree of certainty, that the attendee is attending an event at theevent venue entered into the social media application. Information andstatistics collected by the social media network about the attendee'suse of the MU 110 (search history, social media “likes” or “shares,”chat histories, or any other data collected by social media mobileapplications) that are sent back to the social media networks serversare now sorted into multiple categories. The data from MU 110 are sortedinto a “general population” category 13610 which includes statisticsfrom all of the users of the social network. However, data from a MU 110that has “checked-in” at an event is also sorted into an “eventpopulation” category 13620 which includes statistics and informationfrom all of the other MUs 110 that have checked in at that event. Byknowing which of its users are at a particular event, a social medianetwork, and those it shares its information with, can analyze thedifferences between the information obtained from the social media usersat the event and the “general population” social media users, which willallow for a better understanding of the differences between theindividuals who make up the “general population” and those at the event.

Turning to FIG. 137, there is illustrated a table from a database thatwould be used by social media networks and their affiliates to determinewhich event is occurring at a venue during a particular time. When asocial media user “checks-in” at an event venue, the check-in onlyinforms the social media network that the user is at a particularlocation at a particular time (the check-ins include time-stamps). Thesocial media network, and its affiliates, needs a way to determine whichevent the user is attending. Thus, a relational database is used toassociate an event venue and a time with a specific live event. Anexample of a relational database is illustrated as table 13710. Table13710 lists several different venues (V-1, V-2, and V-3), each as adifferent column 13720 and several different time frames, each as adifferent horizontal row 13730. At the intersection of a row 13720 and acolumn 13730, the table specifies which event is occurring at the venuecorresponding to the column 13730 at the time corresponding to the row13720. For example, at LOAM, Event-A occurring at venue V-1, and noevents are occurring at venues V-2 or V-3. From 12 PM-2 PM, Event-C isoccurring at venue V-2, and Event-B is occurring at venue V-3. Theexample table 13710 illustrated in FIG. 137 lists the events occurringon only one day, but in other embodiments, the tables contained in therelational databases can include multiple days, less than a single day,or any other time frame desired. When a social media user checks-in atan event venue, the check-in venue and check-in time are used to look upin the table 13710 which event the user is attending. For example, if auser checks-in at venue V-2 at 4:30 PM, then the social media networkuses the table 13710 to determine that the user is likely attendingEvent-E.

In addition to indicating when an event starts, the table 13710 can alsospecify the approximate or estimated duration of an event. That way, thesocial media network and its affiliates will have an estimate of whenthe social media user who checked-in at an event likely ceased being atthat event. Knowing this information will allow the social media networkto predict which activity on the user's MU 110 occurred while the userwas at the live event, and which activity occurred while the user wasnot at the live event. Being able to distinguish the two sets ofactivities gives the social media network more knowledge about the userand how (or if) the user's social media habits change while attending alive event.

Turning to FIGS. 138A and 138B, there are illustrated examples of howsocial media data and statistics can be analyzed based on whether a userchecked-in at a live event. FIGS. 138A and 138B both depict pie charts13802. Each pie chart 13802 in this example depicts the proportion ofweb articles “shared” by a set of social media users, broken down by thetype of web article (news-related, sports-related, lifestyle-related, orother). Pie chart 13802 a in FIG. 138A represents the “share”proportions of “general population” users, that is, all of the socialmedia users, including users who have checked-in at an event and userswho have not checked-in at an event. Pie chart 13802 b in FIG. 138Brepresents the proportions of stories “shared” by social media users whochecked-in at Event-X. As can be seen in FIGS. 138A and 138B, theproportions in chart 13802 a differ from the proportions in chart 13802b. For example, news stories were the largest category of stories“shared” by general population social media users, while sports storieswere shared more often by attendees of Event-X.

Other data and statistics can be compared as well. For example, theamount of time spent using social media, the number of posts made, chathabits, and virtually any other category of information can be analyzedwith respect to the differences between general population and eventattendee social media users. Similar comparisons can also be madebetween groups of social media users who attended different live events.

Turning to FIG. 139, there is illustrated an embodiment in which asocial media user “checks-in” into a live event at a venue, causing amodification of advertisements delivered to his social media account orsocial media application. One challenge faced by social media networksand the advertisers who advertise on those networks is knowing the besttimes to advertise to social media users and the best types ofadvertisements to present at any given time. By allowing social media tocheck-in to an event, social networks can alter advertisements andadvertisement delivery to social media users based on the event beingattended.

Referring back to FIG. 139, there is illustrated a depiction of thedisplay of a social media mobile application 13902, as would bedisplayed on a typical mobile device, such as a MU 110. The examplesocial media application 13902 includes a series of posts 13904 postedby various other social media users. Social media application 13902 alsoincludes advertisements 13906 interspersed within the posts 13904. Theseadvertisements are distributed by the social media network to the user'ssocial media account and displayed on his mobile application or webbrowser when viewing his account. Some of the advertisements 13906include “generic” advertisements 13906 a. These advertisements 13906 aare “generic” in the sense that they are delivered to the social mediauser on the basis of having checked-in at a live event. They may,however, be delivered based on the social media user's social mediaactivity, search history, demographic data, or other information.Event-specific advertisements 13906 b, however, are at chosen anddelivered to the social media user at least partially based on the factthat the social media user “checked-in” at a live event. There are anumber of ways checking-in at a live event can affect advertisement ormessage delivery. For example, in some embodiments, advertisements thatare delivered to checked-in users related to the event or type of eventthe user is attending. In other embodiments, the timing of advertisementor message delivery is affected by the event's schedule. These and otherembodiments are described further hereinbelow.

Turning now to FIG. 140, there is illustrated an embodiment in whichadvertisements are delivered to social media users according to aschedule affected by the timing of the live event. One issue faced bysocial media networks delivering advertisements to users is knowing whenusers will most likely view the advertisements. These embodimentsaddress this issue by using event schedules to deliver advertisements ata time when social media users attending events are most likely to beinteracting social media via their mobile devices.

Referring back to FIG. 140, there is illustrated a timeline for anexample event—in this case, a football game. Several points are markedalong the timeline, including the beginning and ending of each quarterof the game. Shaded regions 14004 along the timeline 14002 representperiods of time during which social media users are most likely to beinteracting with social media via their mobile devices, and thus, theoptimal time to present advertisements to the social media accounts. Inthis example, each shaded region 3084 represents time when there is nogameplay happening, such as between quarters, or before and after thegame. These are all times when, since no gameplay is happening, eventattendees are likely to interact with their mobile devices and usesocial media. The optimum advertisement delivery times will vary basedon the type of live event. For example, for baseball games, theadvertisement delivery times might be during the 7th-Inning Stretch, orbetween innings and inning halves. For live music events, the optimumtimes might during periods when the musical talent is resting or whenbetween musical acts. The point is that these embodiments deliveradvertisements to checked-in users on a schedule based at least in parton the schedule of the live event itself.

In some embodiments, the event-based advertisement delivery schedule isbased on an estimated timeline of the event. In these embodiments, thesocial media network has an approximate schedule of when event“down-time” will occur, allowing the social media network to pushadvertisements at these times. These schedules can be obtained by thesocial media network in a similar fashion as is described hereinabovewith respect to determining which event a user is “checking-in” at. Inthese embodiments, social media sites refer to a database having notjust a table 13710 which correlates event venues and times with liveevents, but also having an approximate schedule for any scheduled “downtimes” during the live event. In these embodiments, the social medianetwork corresponds its advertisement delivery schedule to theapproximate schedule.

In some embodiments, not all of the event downtimes are scheduled, orare not scheduled at specific times, meaning that the social medianetwork does not have a way to predict when the optimum advertisementdelivery time is. Examples of these unscheduled downtimes are time-outsor breaks for injuries or player changes during sporting events.Examples of scheduled downtimes without specific times include halftimeduring football games, or the time between periods in hockey games.These events always occur, and an estimated time of the occurrence canbe supplied to the social media network, but their exact timings mightnot be known. In these embodiments, the social media network receivesreal-time updates regarding event downtimes. The social media network isnotified when a downtime starts or is likely to start soon. Once thesocial media network has been notified of an occurring or impendingevent downtime, it can push advertisements to its users checked-in atthat event, or schedule the advertisements for the soon-to-occurdowntime. The downtime notifications can come from several sources. Insome embodiments, the event or event venue operators notify the socialmedia network. The social media network and the notification source eachhave a server, the servers being in communication with each other viathe internet or some other form of communication. The notificationsource's server sends updates to the social media network's servers.These updates can be at regular intervals and/or when a period ofdowntime is impending. In other embodiments, the social network itselfmonitors the event (live, from TV or radio, over the internet, or anyother manner appropriate for monitoring an event) and provides its ownnotifications of upcoming event downtimes.

Turning to FIG. 141, there is illustrated a flowchart depicting theprocess for scheduling advertisements based on a user checking-in at alive event. The process starts at block 14102 and proceeds to functionblock 14104, where the user enters into his mobile device the socialmedia I.D. of the event venue. Next, at block 14106, the mobile device,via a social media mobile application, communicates the venue's socialmedia I.D. to the social media network through a cell network, thevenue's own wireless network, or any other manner in which the socialmedia network can be contacted. Next, at block 14108, the social medianetwork accesses a database and uses the venue's social media I.D. andthe timestamp of the user's check-in transmission to determine whichevent the social media user has checked-in at. Next, at decision block14110, the social media networks checks to the venue-time-event databasefor any scheduled downtime events. If there are no scheduled downtimeevents associated with the live event, the process moves to block 14116,where the social media network receives, via its servers, updates fromthe event operators or venue operators regarding unscheduled downtimesand schedules advertisements to be distributed during these times. Insome embodiments, however, this step is carried out by the social medianetwork itself, rather than by venue or event operators. If, at decisionblock 14110, the social media network does find scheduled downtimes,then the process moves to block 14112, where the social media networkretrieves the schedules of event downtimes. Next, the process moves toblock 14114, where advertisements are scheduled for distribution duringthe known event downtimes. The process then moves to block 14116, asdescribed hereinabove. Next, the process moves to function block 14118.Here, the advertisements are distributed to social media userschecked-in at the event according to the downtime schedules provided bythe venue-time-event database and the downtime updates. Naturally,additional downtime updates might be received by the social medianetwork's servers while the event is underway or after some of thescheduled advertisements have been distributed. In this case, additionaladvertisements are distributed according to the additional updates.Finally, the process ends at block 14120.

In some embodiments, the substance of the advertisements or messagesdelivered to social media users changes as a function of an eventcheck-in. In these cases, social media networks distribute certainadvertisements according to the specific event. For example, if a userchecks-in at a basketball game, the social media network mightdistribute advertisements for shoes, basketball hoops, or otherbasketball-related products or services to the social media user duringthe scheduled time of the game. As another example, a user checking-inat a music concert would cause advertisements for the musical artist'slatest album to be distributed to the user.

In still other embodiments, advertisements deliveries are affected bothby the timing of event downtimes and by the reason for the downtime ortype of downtime. For example, in a baseball game, an injury couldoccur, which would result in an advertisement being delivered during anunscheduled downtime event. The social media network, being informedthat there is an injury downtime, would distribute advertisements tochecked-in users. However, since the social media network knows that thedowntime is the result of an injury, the network would choose anadvertisement for pain reliever, or possibly a health insuranceprovider, to present to its users. Thus, users are presented withadvertisements that are not only advantageously timed, but are alsotopical.

Turning to FIG. 142, there is illustrated an embodiment in which socialmedia users check-in through the use of the UID and the event venue'sown wireless network. These embodiments are similar to those describedhereinabove in FIGS. 136-138, except that event attendees do notcheck-in through social media applications on their MU 110. Instead,these embodiments use a UID which incorporates the attendee's socialmedia information, as is described hereinabove and in FIGS. 128-130.

Referring back to FIG. 142, there is illustrated MU 110, which is incommunication with the event venue's local central office 120, via thevenue's wireless network. At the appropriate time, the MU 110 transmitsthe UID, which includes the social media I.D. information of the MU′ 110owner, to the local central office 120 as described hereinabove. Thelocal central office 120 then transmits the social media information tothe remote central office 126 via the internet 124. In some embodiments,the local central office will only transmit the social media informationextracted from the UIDs, while in other embodiments, the local centraloffice will transmit the entire UID to the remote central office 126.Along with the event attendee's social media information, the localcentral office will also transmit a timestamp of the UID's creation, andinformation designating the venue at which the UID was created. Theremote central office 126 will then transmit to the social media networkservers 14210, via the internet 124, the event attendee's social mediaI.D., the UID timestamp, and the event venue designation. The socialmedia network then uses these three pieces of information and access adatabase relating check-in time and venue location with a specific eventto determine which event the attendee has checked-in at, a processdescribed in more detail hereinabove and in FIGS. 136-138 with respectto social media users providing the same information via social mediaapplications on their mobile devices.

In some embodiments, the mobile unit 110 is a mobile phone. Mobilephones provide an excellent form of mobile unit 110, as they areincreasingly ubiquitous in modern life, with many individuals keepingone (or more) on or about his person at nearly every moment of the day,meaning that most people are very familiar with mobile phones and howthey work. In these embodiments, a mobile application, such as one thatcan be downloaded from popular mobile application repositories such asthe Apple App Store or Google's analogue for its Android operatingsystem, is used by the mobile phone to interact with the system.Advantages to using mobile phones, in addition to the fact that mostevent attendees already own at least one, include the fact that mostmodern mobile phones already include all of the necessary hardware toutilize the disclosed system, such as large screens for viewing responseselection choices, touch-screens for selecting responses, wi-fi antennaefor communicating with the event venue wireless network, and GPS-synchedclocks for accurate time-stamps.

In some embodiments, MUs 110 are used to collect unique ticketinformation within the venue to be used in creating a UID. Using uniqueticket information as part of a UID helps ensure that the UID of eachuser will be unique. The event tickets from which the information iscollected can be physical tickets or virtual tickets displayed on the MU110. The unique ticket information can include ticket locationinformation, such as section-row-seat information. The unique ticketinformation can also be a UID serial number on the ticket, the uniqueserial number of the ticket used to validate that the ticket is not fakeor counterfeit, or a barcode, QR code, or other machine-readable code onthe ticket which is scanned by venue employees at the entrance to thevenue to gain admission to the event (in this case, the code would bescanned by a camera on the mobile unit 110 as a way of entering theinformation into the mobile unit).

In some embodiments, the unique ticket information entered into the MU110 is used to create the UID of the MU 110 and is used to determine theexpected physical location of the attendee using the MU 110. In theseembodiments, the ticket information entered into the MU 110 includesinformation about the geographic location of the attendee's designatedseat or seating area, such as section/row/seat information, or amulti-person area within a venue, such as a “standing-room only”section. The local central office 120 can translate the informationcontained within an attendee's UID determine where the attendee for agiven UID is designated to be seated. This can be useful, for instance,when trying to determine if attendees' location within the venue affectshow or if they respond to query presentations, or if certaindemographics tend to be seated in certain areas of the venue. Attendeescan also be grouped and analyzed based on their expected physicallocation within the event venue.

In some embodiments, expected attendee location information is used todeliver items such as food, merchandise, prizes, or promotional items toevent attendees. For example, an event may hold a prize give-awaydrawing which attendees can enter by participating in the query andresponse system. Using the location information obtained from translatedUID information, an attendee's seat location, and therefore his expectedphysical location, can be determined. Prizes can then be delivered tohim at his seat in the event venue during the event. The delivery canalso be recorded and broadcast within the venue to increase excitementand participation in the query and response system among otherattendees. In some embodiments, an attendee can purchase items, such asfood or gifts, during the live event using his mobile device. Theattendee's expected location is determined from information obtainedfrom his UID, and then the items are delivered to the attendee at hisseat or an area near his seat in the venue during the event.

In some embodiments, visual cues are used to prompt event attendees tointeract with their mobile devices. Event attendees who want toparticipate in the query and response system would benefit from an earlycue or a “heads-up” that a query is going will soon be presented,allowing them time to retrieve their mobile devices or in other ways getready to adequately respond to the presented query. These embodimentsaddress this concern by presenting visual cues within a videopresentation to alert the attendees of an upcoming query or other mobiledevice engagement opportunity. Many live events already feature videomessages presented on large video screens within the venue. Thus, visualcues are simply inserted into pre-existing video messages to alertattendees of an upcoming query. The visual cues can also be insertedinto presentations which form the premise of a query that is yet to bepresented. These embodiments also allow for inserted visual cues toprompt attendees to interact with their mobile devices for any number ofreasons that are useful for collecting statistically significantinformation. For example, visual cues can also be presented to promptevent attendees to use their mobile devices to “check-in” to the eventor event venue via social media applications, or to visit a website andanswer questions or vote on some topic of interest to event attendees.

Although the preferred embodiment has been described in detail, itshould be understood that various changes, substitutions and alterationscan be made therein without departing from the spirit and scope of theinvention as defined by the appended claims.

What is claimed is:
 1. A method for interacting with audience members inan event, which is defined as being at a physical venue location andoccurring at a particular predetermined time, wherein the event has afinite number of participating attendees and an associated finite numberof unique potential attendee identifiers (UPAIs) allocated to anyparticular event, which UPAIs can be replicated for other events atother times in the same physical venue location or at events in othervenue locations, selected from attendees of the event, each of theparticipating attendees having available thereto one of the UPAIs forthe associated event, comprising the steps of: creating, for eachparticipating attendee, when the attendee is in physical proximity tothe venue location associated with the event and proximate in time tothe occurrence of the particular predetermined time of the event and isprompted via an external prompting source proximate in time and locationto the event to enter a UPAI into a mobile wireless device (MWD), aunique ID (UID) on the MWD by the steps of: inputting to the MWD one ofthe UPAIs; and combining the received UPAI with a UID time stamp at thetime of input of the UPAI in the MWD to provide the UID comprised of aUPAI portion and a UID time stamp portion so as to distinguish twodifferent UIDs having a duplicate UPAI portion, resulting in a verifiedUID; receiving with a server on a first wireless channel communicationsfrom the MWD of each participating attendee and having associatedtherewith one of the verified UIDs; registering the verified UID of eachparticipating attendee at the physical venue location of the event todefine registered attendees; associating a respective social network IDwith each respective UID; generating at the server a visual query;displaying on a physical display at the event the visual query, whereinthe physical display is visible by at least a plurality of finite numberof participating attendees and wherein the visual query relates to aplurality visual messages; displaying response indicators on the MWD ofa plurality of the registered attendees; inputting, by a plurality ofthe respective registered attendees on which the response indicatorswere displayed, a response via selection of one of the MWD responseindicators on the respective MWD associated with the respectiveregistered attendee; receiving at the server from each of the respondingones of the registered attendees the selected responses to the queryover the first wireless channel; storing in a database on the server thereceived responses in association with the displayed query; associatingthe respective received responses with the respective verified UIDs;creating a query ID (QID) associated with the displayed query; creatingrespective response time stamp indicating the respective times at whichthe respective responses were received by the server; creating a datarecord for the each verified UID, each verified UID data recordincluding: the respective verified UID, the associated social networkID, the QID, the respective received response, and the respectiveresponse time stamp; creating a data record for the QID, the QID datarecord including: the QID; each of the respective received responses,each respective verified UID, and each respective response time stamp;performing statistical analysis on the QID data record; selecting, basedon the statistical analysis of the QID data record, one of the visualmessages from the plurality of visual messages; and displaying on thephysical display at the event the selected visual message; wherein thestatistical analysis relates to the social network ID associated witheach respective verified UID.
 2. The method of claim 1, wherein theevent is a closed venue.
 3. The method of claim 1, wherein each UPAI isa respective fixed location associated with the venue, each UPAI beingassociated with a different fixed location within the venue.
 4. Themethod of claim 3, wherein each respective fixed location is a definedseat in a closed venue having a seat identifier as the location.
 5. Themethod of claim 1, wherein registering each respective verified UIDcomprises: recognizing each respective MWD at a predetermined and fixedlocation within the physical location of the event; and launching anapplication on each respective MWD followed by the steps of promptingeach participating attendee to input a UPAI into the MWD.
 6. The methodof claim 5, wherein recognizing each respective MWD comprises a scan ofthe MWD in the vicinity of the predetermined and fixed location.
 7. Themethod of claim 1, wherein registering each respective verified UIDcomprises responding to a request for registration by transmitting therespective verified UID to a server over the first wireless channel. 8.The method of claim 1, wherein the response indicators comprisealphabetic symbols.
 9. The method of claim 1, wherein the responseindicators comprise numeric symbols.
 10. The method of claim 1, whereinthe response indicators comprise symbols of differing colors.
 11. Themethod of claim 1, wherein receiving at the server a select responseincludes receiving a timestamp associated with the response.
 12. Themethod of claim 1, wherein receiving at the server select responsesincludes receiving the responses within a defined time window.
 13. Themethod of claim 1, further comprising associating each respectivereceived response with statistical information related to an attendeeclassification.
 14. The method of claim 1, further comprising assigningeach respective verified UID to a statistical category based on therespective received response and the displayed query.
 15. The method ofclaim 1, wherein the verified UID data record also includes statisticalinformation that is related to an attendee classification and that isbased on the received response.
 16. A system for interacting withaudience members in an event at a defined venue, wherein the event has afinite number of potential attendees, each of the potential attendeeshaving available thereto a unique identifier for association therewith,the system comprising: a first wireless channel; a mobile wirelessdevice (MWD) for each potential attendee, each respective MWD configuredto display response indicators and to communicate responses over thefirst wireless channel; a unique ID (UID) created on the respective MWD,each UID comprising: a unique identifier available to the respectiveattendee in association with the associated MWD for that potentialattendee to define a portion of the UID; and a time stamp indicating thetime of creation of the respective UID on the respective MWD as a secondportion of the UID; a registration device for interfacing the MWD with aserver to allow the MWD to transmit the associated UID to the server forverification as a verified UID; the server configured to generate avisual query and to receive communications from each MWD over the firstwireless channel, the server having: a filter for filtering the receivedUIDs into verified UIDs, such that any UID having an identical firstportion from two or more different MWDs will use the time stampinformation in the associated second portion to verify only the receivedUID as a verified UID for the earlier received one thereof; a physicaldisplay for displaying at least one of a plurality of visual messagesand the visual query; and a database, on the server, configured to storethe plurality of visual messages and to store responses that arereceived from the MWD associated with verified UIDs and that areassociated with the displayed visual query; wherein each respectiveverified UID has associated therewith a respective social network ID;wherein the server is also configured to perform a statistical analysison the stored responses, to select, based on the statistical analysis ofthe stored responses, one of the plurality of visual messages from theplurality of visual messages, and to communicate the visual message tothe physical display; and wherein the statistical analysis relates tothe respective social network ID associated with each respective UID.17. The system of claim 16, wherein the event is a closed venue.
 18. Thesystem of claim 16, wherein the MWD is also configured to communicatetimestamps indicating the time at which the responses are communicatedover the first wireless channel.
 19. The system of claim 16, whereineach unique identifier comprises a respective fixed location associatedwith the venue.
 20. The system of claim 19, wherein each respectivefixed location comprises a defined seat in a closed venue having a seatidentifier as the location.